DeepHealth: Review and challenges of artificial intelligence in health informatics

Gloria Hyunjung Kwak; Pan Hui

REVIEW 3 major objections 5 minor 300 references

DeepHealth: Review and challenges of artificial intelligence in health informatics

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This review maps roughly seven years of artificial-intelligence research across medical imaging, electronic health records, genomics, sensing, and online communication health, and consolidates the data and model barriers that still stand…

desk verdict A serviceable survey that overclaims comprehensiveness: useful for newcomers, not a new result, and it needs a documented search protocol and a fixed abstract. read the letter →

arxiv 1909.00384 v2 pith:QDA7K73N submitted 2019-09-01 cs.LG cs.CVeess.IVstat.ML

Gloria Hyunjung Kwak , Pan Hui This is my paper · ORCID

classification cs.LGcs.CVeess.IVstat.ML

keywords artificialintelligencehealthinformaticsdeeplearningmedicalimagingelectronicrecordsgenomicswearablesensingonlinecommunities

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to give a comprehensive map of artificial-intelligence research in health informatics over roughly the last seven years, organized around five application areas: medical imaging, electronic health records, genomics, sensing, and online communication health. It argues that deep learning in particular has shifted health-AI work away from hand-crafted feature extraction, enabling disease diagnosis, outcome prediction, genotype-phenotype analysis, treatment recommendations, and outbreak forecasting. The review then assembles the recurring obstacles into two buckets: data-side problems (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, missing labels, bias) and model-side problems (reliability, interpretability, feasibility, security, scalability). A sympathetic reader would take the paper's contribution to be the consolidated landscape plus a usable challenge taxonomy, rather than any single new algorithm or result.

What carries the argument

The organizing machinery is a three-part grid: data modality (imaging, EHR, genomics, sensing, online) times task type (classification, segmentation, prediction, extraction, generation, recommendation) times model family (CNN, RNN, autoencoders, DBN, attention, transfer learning, reinforcement learning). The grid carries the argument by letting the review show that the same model families recur across very different data, and that a single data/model challenge taxonomy explains why most systems stop short of the clinic.

What would settle it

A preregistered systematic search of the same five domains over 2013-2020 that applied explicit inclusion and exclusion criteria would settle whether the review's catalogue is comprehensive; if such a search surfaced major untouched research lines, or showed the cited papers to be concentrated in a few well-resourced settings, the paper's map and its challenge taxonomy would need revision.

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Extended reading notes

Core claim

The authors claim that the past seven years have produced a recognizable set of deep-learning practices in each of five data modalities, and that the same short list of unsolved problems repeats across all of them. The review classifies the field's tasks—classification, segmentation, detection, outcome prediction, phenotyping, information extraction, representation learning, de-identification, intervention recommendation, gene and epigenome prediction, drug generation, biosignal monitoring, and social-media health surveillance—and matches each to the model families that dominate it: CNN and multi-stream, 2.5D, and 3D variants for imaging; word embeddings, RNN, LSTM, GRU, and attention for electronic health records; CNN, DBN, and autoencoders for genomics; CNN and deep deterministic learning for sensing; and natural-language models for online health data. The claim is descriptive: these applications have reached clinical alternative technology levels in some areas, but data scarcity, missing labels, bias, irreproducible pre-processing, black-box interpretability, feasibility, security, and scalability remain unsolved.

Load-bearing premise

The map's completeness assumes that the informally selected papers in Section 3 fairly represent the field over the past seven years, since no database list, search date range, inclusion criteria, or screening protocol is reported.

Editorial extensions

If this is right

Transfer learning and multi-stream architectures should be treated as the default scaffolding for medical-imaging models when annotated volumes are scarce, since the review finds them consistently outperforming single-stream training from scratch.
In EHR research, temporal modeling with RNN, LSTM, GRU, and attention, together with explicit missingness handling, is the prevailing route to outcome prediction and phenotyping.
Genomics and drug-design work increasingly relies on unsupervised representation learning, generative models, and reinforcement learning to compensate for high-dimensional, sparsely labeled molecular data.
Data pre-processing choices are themselves a source of bias, so the review implies that fully reporting them is a precondition for comparing models' true clinical performance.
Multi-modal and multi-task learning, plus on-device or privacy-preserving deployment, are the stated future directions for moving these systems from retrospective datasets toward prospective clinical use.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Extension: the 'comprehensive' label is best read as comprehensive within the five chosen domains, since adjacent areas such as nursing documentation and dental imaging are not covered.
Extension: if the data/model challenge taxonomy is stable, progress on interpretability in one domain should transfer to the others, which a cross-domain method-transfer experiment could test cheaply.
Extension: the emphasis on retrospective cohorts and shifting clinical protocols implies that prospective, multi-institution validation is the binding constraint on clinical adoption, not model capacity.
Extension: the credibility concerns raised for sensing and online data suggest privacy-preserving approaches will be a precondition for the social-media health surveillance strand.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a competent literature review with two caveats that matter — the "comprehensive" label is not backed by a reported search protocol, and the abstract contradicts the body on data pre-processing.

What's actually new: not much as a scientific result, which is normal for a survey. It covers five application areas — imaging, EHR, genomics, sensing, and online communication health — and that last pair gives it slightly more breadth than earlier reviews. The model overview in Section 2 is clear, and the challenge taxonomy (data vs model; high-dimensionality, bias, interpretability, feasibility, scalability) is sensible. The reference list is broad and mostly fits the claims it supports. This is a fine orientation piece for a graduate student entering the field.

Soft spots, in proportion. First, the central claim. The abstract promises a "comprehensive review" of the last seven years, but Section 3 says only that the authors "searched across several databases" and briefly reviewed "significantly relevant papers." No databases, no search date range, no inclusion/exclusion criteria, no screening counts. That means the reviewed corpus cannot be checked for representativeness. I see no evidence the selection is actually skewed, but the method doesn't rule it out, and the claim is load-bearing. Second, an internal contradiction: the abstract says recent AI approaches "do not require domain-specific data pre-processing," while Section 4.1.2 says pre-processing is important and "still a blind exploration process." The body is right; the abstract should be fixed. Third, the online communication health section is noticeably thinner than the EHR and imaging sections, so the breadth claim is uneven. Minor point: the only self-citation, [187], appears as an example in the EHR review and is not load-bearing. Citation pattern looks fine.

Verdict: this is not a new result; it is a synthesis that overlaps with earlier reviews such as [1-3,6,8,9]. It would help a newcomer, and with an honest title and a documented method it could be a useful published survey. I would send it to peer review — not because it is groundbreaking, but because the scope is broad and the references serious enough that a referee can push the authors to either justify or retract the comprehensiveness claim.

Referee Report

3 major / 5 minor

Summary. This preprint is a narrative review of artificial intelligence applications in health informatics. It covers five application areas: medical imaging, electronic health records, genomics, sensing, and online communication health. The paper gives an overview of common models, including SVM, tensor decomposition, word embeddings, CNNs, RNNs, autoencoders, deep belief networks, attention, transfer learning, and reinforcement learning. It then surveys representative studies in each application area and concludes with a taxonomy of data-side and model-side challenges, including high dimensionality, heterogeneity, missing labels, bias, interpretability, reliability, feasibility, security, and scalability. The abstract's headline claim is that the article is a comprehensive review of AI research in health informatics over roughly the last seven years.

Significance. If the review were comprehensive and accurate, it would provide a useful map of a fast-moving field, and its taxonomy of challenges would be a convenient reference for researchers entering health informatics. The paper has real strengths: it collects a large number of representative studies, organizes them by data modality and task, and accompanies them with a self-contained model overview including formal descriptions of SVM, tensor decomposition, and neural architectures. The challenges section (Section 4) is broad and reflects concerns that are widely discussed in the literature, such as data bias, missingness, interpretability, and scalability. However, the paper's value as a 'comprehensive review' is limited by the absence of a reproducible paper-selection protocol, and the abstract's claim that recent AI approaches 'do not require domain-specific data pre-processing' is internally inconsistent with Section 4.1.2. These issues affect the central characterization of the reviewed literature and the derived challenge list, though they are fixable in revision.

major comments (3)

[Section 3, first paragraph] The load-bearing claim of 'comprehensive review' is not supported by the reported search method. The paper says the authors 'searched across several databases with the combination of search terms' and then 'significantly relevant papers ... were brieﬂy reviewed,' but it does not state which databases were searched, what date range was applied, how relevance was judged, or how many records were screened and excluded. Without this information, no reader can verify that the 300+ cited papers are representative of the literature in medical imaging, EHR, genomics, sensing, and online communication health rather than a selected sample. This is not a cosmetic omission: the application catalogue in Section 3 and the challenge taxonomy in Section 4 are both presented as field-wide conclusions, so the selection bias risk propagates to the paper's main claims. I recommend adding a reproducible search-and-screening protocol, for example a PRISMA-style flow diagram with database names, search dates, inclusion/exclusion criteria, and screening counts, or explicitly relabeling the article as a narrative review rather than a comprehensive one.
[Abstract vs. Section 4.1.2] The abstract states that 'recent artiﬁcial intelligence approaches do not require domain-speciﬁc data pre-processing,' but Section 4.1.2 argues that pre-processing is important for high-dimensional, sparse, irregular, and biased data and that 'pre-processing, normalization or change of input domain, class balancing and hyperparameters of models are still a blind exploration process.' Section 3.3 similarly notes that genomic datasets are 'incredibly high dimensional, heterogeneous, and unbalanced' and that domain experts' pre-processing 'was frequently required.' The abstract's claim is therefore contradicted by the paper's own detailed discussion. This inconsistency should be resolved by either removing or qualifying the abstract statement, since as written it misrepresents the review's substantive content.
[Section 3 and Figure 1] The paper claims to focus on 'the last seven years,' but the text does not specify the search period, and the reference list includes foundational works from before 2013 (e.g., LeNet [64], LSTM [73], SVM [57], and the original word2vec paper [62]). It is possible that these older works are included as background for model descriptions, but the review does not distinguish background citations from papers that fall within the claimed seven-year window. Figure 1 also reports a distribution of published papers from PubMed without stating the query, date range, or deduplication method used. I ask the authors to clarify the temporal scope and to state explicitly which papers are part of the 'last seven years' corpus and which are cited only as background.

minor comments (5)

[Equation (2)] The sentence following Equation (2), 'Having a regularization term ||w||2 and a small value parameter makes data can be ﬁnally linearly classiﬁable,' is grammatically unclear and should be rewritten to explain the role of the regularization parameter and the soft margin.
[Section 3.3.1] In the paragraph discussing genetic variants, the text says 'Reference [212]' for the LPA SNP study; this same reference is already discussed in Section 3.2.2 as an EHR topic-modeling study. The cross-reference is acceptable but the sentence could more clearly indicate that this is a genomics example using EHR-derived phenotypes.
[Throughout] The paper uses 'traditional models' and 'domain-speciﬁc data pre-processing' in the abstract but 'pre-processing' and 'feature extraction' elsewhere; the terminology should be harmonized so that the discussion of Section 4.1.2 is not confused with the more general claims in the introduction.
[Figure 1] The caption of Figure 1 says 'from PubMed' but gives no query details, date of search, or number of papers considered; please provide this information or remove the quantitative-sounding distribution claim.
[References] Some references are incomplete or informal, such as [61] and [76], which cite Wikipedia pages without author names or version dates, and [187], a preprint under review. Please check all references for completeness and ensure published versions are cited where available.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: this is a secondary literature review with no derivation loop, fitted prediction, or load-bearing self-citation.

full rationale

The paper is a narrative review of artificial intelligence applications in health informatics. Its central claim is that it provides a comprehensive review over the last seven years across five areas, along with challenges and future directions. There is no mathematical derivation, no model fitted to data, and no quantity predicted from an input that is itself defined in terms of the output. The selection of papers in Section 3 is described qualitatively: 'we searched across several databases with the combination of search terms' and 'significantly relevant papers regarding each part with applying DL algorithms were briefly reviewed.' This methodological reporting is incomplete and makes the comprehensiveness claim hard to verify, but incomplete reporting is a rigor or reproducibility concern, not circularity. The only self-citation, reference [187], appears as an example of data bias work in Section 3.2.1 and in the reference list; it is not used as evidence for any theorem, uniqueness claim, or benchmark, and the review's content does not depend on it. The abstract's assertion that recent AI approaches 'do not require domain-specific data pre-processing' is contradicted by Section 4.1.2, which emphasizes that preprocessing remains a 'blind exploration process.' This is an internal inconsistency that affects accuracy, but it is not a circular step because neither statement is derived from the other or from a fitted input. No pattern from the enumerated circularity kinds is present: there is no self-definitional relationship, no fitted input called a prediction, no load-bearing self-citation, no imported uniqueness theorem, no ansatz smuggled in via citation, and no renaming of a known result as a new organization. The paper is a secondary synthesis of external, independently published results, so the honest finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

No free parameters exist because the paper makes no derivation or fit. Two domain assumptions are load-bearing: the reviewed papers represent the field, and the secondary performance claims are copied accurately from the original papers. No new entities are introduced. The paper's own text in Section 3 provides only vague search terms, so the representativeness assumption is not independently evidenced.

assumptions (2)

domain assumption Selected literature is representative of the state of AI in health informatics.
The review's "comprehensive" claim depends on coverage; Section 3 does not specify inclusion and exclusion criteria, so representativeness is assumed rather than shown.
domain assumption Reported performance numbers from cited studies are faithfully transcribed.
The review does not re-run experiments or verify numbers such as the 23% AUC gain in Section 3.2.1, so the accuracy of this secondary synthesis depends on the accuracy of its sources.

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Cite this review

Pith. "Pith review of DeepHealth: Review and challenges of artificial intelligence in health informatics." pith.science (2026). https://pith.science/paper/QDA7K73N

@misc{pith2026190900384,
  author       = {Pith},
  title        = {Pith review of: DeepHealth: Review and challenges of artificial intelligence in health informatics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QDA7K73N}},
  note         = {Machine review of arXiv:1909.00384}
}

read the original abstract

Artificial intelligence has provided us with an exploration of a whole new research era. As more data and better computational power become available, the approach is being implemented in various fields. The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its applications in healthcare. It can help clinicians diagnose disease, identify drug effects for each patient, understand the relationship between genotypes and phenotypes, explore new phenotypes or treatment recommendations, and predict infectious disease outbreaks with high accuracy. In contrast to traditional models, recent artificial intelligence approaches do not require domain-specific data pre-processing, and it is expected that it will ultimately change life in the future. Despite its notable advantages, there are some key challenges on data (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, lack of label, bias) and model (reliability, interpretability, feasibility, security, scalability) for practical use. This article presents a comprehensive review of research applying artificial intelligence in health informatics, focusing on the last seven years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research. We highlight ongoing popular approaches' research and identify several challenges in building models.

Figures

Figures reproduced from arXiv: 1909.00384 by the authors.

**Figure 1.** Left: Distribution of published papers that use artificial intelligence in subareas of health informatics from [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** CP decomposition of a three-way array [60] [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** CBOW and Skip-gram [62] 2.4 Multilayer Perceptron Perceptron is an ML algorithm that researchers refer to as the first online learning algorithm. Multilayer perceptron (MLP) is a feedforward neural network that has perceptrons (neurons) for each layer [23]. When a model has three layers, the minimum amount of layers, the network is called either vanilla or shallow neural network, and when it is deeper than three lay… view at source ↗

Figures from the paper (16 more)

**Figure 4.** Figure 4: Left: The architecture of AlexNet (2D CNN) [65], Right: The architecture of 3D CNN [67] [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Left: Detailed schematic of the Simple Recurrent Network (SRN) unit, Right: The architecture of a Long [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: Left: Autoencoder, Right: Stacking Denoising Autoencoder [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 7.** Figure 7: Left: Restricted Boltzmann Machine with four visible nodes and three hidden nodes, Right: Three-layer Deep [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 8.** Figure 8: The encoder-decoder model with additive attention mechanism [87] [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 9.** Figure 9: The agent-environment interaction in reinforcement learning [92] [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

**Figure 10.** Figure 10: Slices of an MRI scan of an AD patient, from Left to Right: in axial view, coronal and sagittal views [100] [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]

**Figure 11.** Figure 11: The multi-angle and multi-scale CNN architecture for pulmonary nodule classification [137] [PITH_FULL_IMAGE:figures/full_fig_p010_11.png]

**Figure 12.** Figure 12: The architecture of the single- and multi-modality network for Alzheimer’s disease [143] [PITH_FULL_IMAGE:figures/full_fig_p010_12.png]

**Figure 13.** Figure 13: ICD-9-CM diagnosis codes of acute kidney injury [PITH_FULL_IMAGE:figures/full_fig_p012_13.png]

**Figure 14.** Figure 14: Sample of pre-processed lab events data; Each row represents a visit to the clinic. [PITH_FULL_IMAGE:figures/full_fig_p012_14.png]

**Figure 15.** Figure 15: Sample of the frequency of allele combination; ex. 0.087 for AA (TT) and 0.912 for Aa (TC) or aA (CT). p [PITH_FULL_IMAGE:figures/full_fig_p015_15.png]

**Figure 16.** Figure 16: Systems biology strategies that integrate large-scale genetic, intermediate molecular phenotypes and disease [PITH_FULL_IMAGE:figures/full_fig_p019_16.png]

**Figure 17.** Figure 17: Alternative splicing produces three protein isoforms [269]. [PITH_FULL_IMAGE:figures/full_fig_p019_17.png]

**Figure 18.** Figure 18: Schematic overview of the remote health monitoring system with the most used possible sensors worn on [PITH_FULL_IMAGE:figures/full_fig_p022_18.png]

**Figure 19.** Figure 19: Adjusted survival curve stratified by timing of completion of AKI care bundle [326] [PITH_FULL_IMAGE:figures/full_fig_p026_19.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

300 extracted references · 58 canonical work pages

[187]

Predicting need for vasopressors in the intensive care unit using an attention based deep learning model

Gloria Hyunjung Kwak, Lowell Ling, and Pan Hui. Predicting need for vasopressors in the intensive care unit using an attention based deep learning model. researchSquare preprint doi:10.21203/rs.3.rs-45876/v1, 2020

work page doi:10.21203/rs.3.rs-45876/v1 2020
[64]

Gradient-based learning applied to document recognition

Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998

1998
[73]

Long short-term memory

Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997

1997
[57]

Support vector machine

V Vapnik. Support vector machine. Mach. Learn, 20:273–297, 1995

1995
[62]

Efﬁcient estimation of word representations in vector space

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efﬁcient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013

arXiv 2013
[1]

Deep learning for healthcare: review, opportunities and challenges

Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T Dudley. Deep learning for healthcare: review, opportunities and challenges. Brieﬁngs in bioinformatics, 19(6):1236–1246, 2017

2017
[2]

Deep learning for health informatics

Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, and Guang- Zhong Yang. Deep learning for health informatics. IEEE journal of biomedical and health informatics , 21(1):4–21, 2016

2016
[3]

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez. A survey on deep learning in medical image analysis. Medical image analysis, 42:60–88, 2017

2017

Show all 300 references

[4]

Biomarker tests for molecularly targeted therapies–the key to unlocking precision medicine

Gary H Lyman and Harold L Moses. Biomarker tests for molecularly targeted therapies–the key to unlocking precision medicine. The New England journal of medicine, 375(1):4, 2016

2016
[5]

A new initiative on precision medicine

Francis S Collins and Harold Varmus. A new initiative on precision medicine. New England journal of medicine, 372(9):793–795, 2015

2015
[6]

Shickel, P

B. Shickel, P. J. Tighe, A. Bihorac, and P. Rashidi. Deep ehr: A survey of recent advances in deep learning techniques for electronic health record (ehr) analysis. IEEE Journal of Biomedical and Health Informatics , 22(5):1589–1604, Sep. 2018

2018
[7]

Deep learning for computational biology

Christof Angermueller, Tanel Pärnamaa, Leopold Parts, and Oliver Stegle. Deep learning for computational biology. Molecular systems biology, 12(7), 2016

2016
[8]

A guide to deep learning in healthcare

Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, V olodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, and Jeff Dean. A guide to deep learning in healthcare. Nature medicine, 25(1):24, 2019

2019
[9]

A systematic literature review of machine learning in online personal health data

Zhijun Yin, Lina M Sulieman, and Bradley A Malin. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association, 26(6):561–576, 2019

2019
[10]

Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey

Siqi Liu, Kee Yuan Ngiam, and Mengling Feng. Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey. arXiv e-prints, page arXiv:1907.09475, Jul 2019

1907 arXiv
[11]

Deep learning for healthcare biometrics

Upendra Kumar, Esha Tripathi, Surya Prakash Tripathi, and Kapil Kumar Gupta. Deep learning for healthcare biometrics. In Design and Implementation of Healthcare Biometric Systems, pages 73–108. IGI Global, 2019. 26 A PREPRINT - AUGUST 11, 2020

2019
[12]

Deep learning trends for focal brain pathology segmentation in mri

Mohammad Havaei, Nicolas Guizard, Hugo Larochelle, and Pierre-Marc Jodoin. Deep learning trends for focal brain pathology segmentation in mri. In Machine learning for health informatics , pages 125–148. Springer, 2016

2016
[13]

Big data application in biomedical research and health care: a literature review

Jake Luo, Min Wu, Deepika Gopukumar, and Yiqing Zhao. Big data application in biomedical research and health care: a literature review. Biomedical informatics insights, 8:BII–S31559, 2016

2016
[14]

Biomedical informatics and machine learning for clinical genomics

James A Diao, Isaac S Kohane, and Arjun K Manrai. Biomedical informatics and machine learning for clinical genomics. Human Molecular Genetics, 27, 03 2018

2018
[15]

Deep learning in drug discovery

Erik Gawehn, Jan A Hiss, and Gisbert Schneider. Deep learning in drug discovery. Molecular informatics, 35(1):3–14, 2016

2016
[16]

Deep learning: new computational modelling techniques for genomics

Gökcen Eraslan, Žiga Avsec, Julien Gagneur, and Fabian J Theis. Deep learning: new computational modelling techniques for genomics. Nature Reviews Genetics, page 1, 2019

2019
[17]

Deep artiﬁcial neural networks and neuromorphic chips for big data analysis: pharmaceutical and bioinformatics applications

Lucas Pastur-Romay, Francisco Cedron, Alejandro Pazos, and Ana Porto-Pazos. Deep artiﬁcial neural networks and neuromorphic chips for big data analysis: pharmaceutical and bioinformatics applications. International journal of molecular sciences, 17(8):1313, 2016

2016
[18]

A review and assessment framework for mobile-based emergency intervention apps

Michal Yablowitz and David Schwartz. A review and assessment framework for mobile-based emergency intervention apps. ACM Computing Surveys, 51:1–32, 01 2018

2018
[19]

A review of wearable sensors and systems with application in rehabilitation

Shyamal Patel, Hyung-Soon Park, Paolo Bonato, Leighton Chan, and Mary Rodgers. A review of wearable sensors and systems with application in rehabilitation. Journal of neuroengineering and rehabilitation, 9:21, 04 2012

2012
[20]

Critical care, critical data.Biomedical Engineering and Computational Biology, 10:1179597219856564, 2019

Christopher V Cosgriff, Leo Anthony Celi, and David J Stone. Critical care, critical data.Biomedical Engineering and Computational Biology, 10:1179597219856564, 2019

2019
[21]

Deep learning in neural networks: An overview

Jürgen Schmidhuber. Deep learning in neural networks: An overview. Neural networks, 61:85–117, 2015

2015
[22]

Dropout: a simple way to prevent neural networks from overﬁtting

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overﬁtting. The journal of machine learning research, 15(1):1929– 1958, 2014

1929
[23]

Deep learning

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436, 2015

2015
[24]

3d deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients

Dong Nie, Han Zhang, Ehsan Adeli, Luyan Liu, and Dinggang Shen. 3d deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 212–220. Springer, 2016

2016
[25]

Multimodal deep learning for cervical dysplasia diagnosis

Tao Xu, Han Zhang, Xiaolei Huang, Shaoting Zhang, and Dimitris N Metaxas. Multimodal deep learning for cervical dysplasia diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 115–123. Springer, 2016

2016
[26]

Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications

Hang Chang, Ju Han, Cheng Zhong, Antoine M Snijders, and Jian-Hua Mao. Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications. IEEE transactions on pattern analysis and machine intelligence, 40(5):1182–1194, 2017

2017
[27]

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

Ravi K Samala, Heang-Ping Chan, Lubomir Hadjiiski, Mark A Helvie, Jun Wei, and Kenny Cha. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. Medical physics, 43(12):6654–6666, 2016

2016
[28]

Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition

Zhennan Yan, Yiqiang Zhan, Zhigang Peng, Shu Liao, Yoshihisa Shinagawa, Shaoting Zhang, Dimitris N Metaxas, and Xiang Sean Zhou. Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition. IEEE transactions on medical imaging, 35(5):1332–1343, 2016

2016
[29]

Machine learning in genomic medicine: a review of computational problems and data sets

Michael KK Leung, Andrew Delong, Babak Alipanahi, and Brendan J Frey. Machine learning in genomic medicine: a review of computational problems and data sets. Proceedings of the IEEE, 104(1):176–197, 2015

2015
[30]

The human splicing code reveals new insights into the genetic determinants of disease

Hui Y Xiong, Babak Alipanahi, Leo J Lee, Hannes Bretschneider, Daniele Merico, Ryan KC Yuen, Yimin Hua, Serge Gueroussov, Hamed S Najafabadi, Timothy R Hughes, et al. The human splicing code reveals new insights into the genetic determinants of disease. Science, 347(6218):1254...

2015
[31]

Predicting the sequence speciﬁcities of dna-and rna-binding proteins by deep learning

Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey. Predicting the sequence speciﬁcities of dna-and rna-binding proteins by deep learning. Nature biotechnology, 33(8):831, 2015

2015
[32]

Accurate prediction of single-cell dna methylation states using deep learning

Christof Angermueller, Heather J Lee, Wolf Reik, and Oliver Stegle. Accurate prediction of single-cell dna methylation states using deep learning. BioRxiv, page 055715, 2016. 27 A PREPRINT - AUGUST 11, 2020

2016
[33]

A clinically applicable approach to continuous prediction of future acute kidney injury

Nenad Tomašev, Xavier Glorot, Jack W Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature, 572(7767):116, 2019

2019
[34]

Davis, Thomas A

Sharon E. Davis, Thomas A. Lasko, Guanhua Chen, Edward D. Siew, and Michael E. Matheny. Calibration drift in regression and machine learning models for acute kidney injury. Journal of the American Medical Informatics Association, 24(6):1052–1061, nov 2017

2017
[35]

Seymour, Jason N

Christopher W. Seymour, Jason N. Kennedy, Shu Wang, Chung-Chou H. Chang, Corrine F. Elliott, Zhongying Xu, Scott Berry, Gilles Clermont, Gregory Cooper, Hernando Gomez, David T. Huang, John A. Kellum, Qi Mi, Steven M. Opal, Victor Talisa, Tom van der Poll, Shyam Visweswaran, Y...

2019
[36]

Knaus and Richard D

William A. Knaus and Richard D. Marks. New Phenotypes for Sepsis. JAMA, may 2019

2019
[37]

Improving outcomes in patients with acute kidney injury: the impact of hospital based automated aki alerts

M Prendecki, E Blacker, O Sadeghi-Alavijeh, R Edwards, H Montgomery, S Gillis, and M Harber. Improving outcomes in patients with acute kidney injury: the impact of hospital based automated aki alerts. Postgraduate Medical Journal, 92(1083):9–13, 2016

2016
[38]

Eric A. J. Hoste, Kianoush Kashani, Noel Gibney, F. Perry Wilson, Claudio Ronco, Stuart L. Goldstein, John A. Kellum, Sean M. Bagshaw, and on behalf of the 15 ADQI Consensus Group. Impact of electronic-alerting of acute kidney injury: workgroup statements from the 15th adqi co...

2016
[39]

Nephrotoxicities

Stuart L Goldstein. Nephrotoxicities. F1000Research, 6:55, jan 2017

2017
[40]

Review of vancomycin-induced renal toxicity: an update

Oluwatoyin Bamgbola. Review of vancomycin-induced renal toxicity: an update. Therapeutic advances in endocrinology and metabolism, 7(3):136–147, jun 2016

2016
[41]

Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation

Lu Wang, Wei Zhang, Xiaofeng He, and Hongyuan Zha. Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2447–2456. ACM, 2018

2018
[42]

A clinically applicable approach to continuous prediction of future acute kidney injury

Nenad Tomašev, Xavier Glorot, Jack W Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature, 572(7767):116–119, 2019

2019
[43]

General practitioner-hospital commu- nications: A review of discharge summaries

Stephen Wilson, Warwick Ruscoe, Margaret Chapman, and Rhona Miller. General practitioner-hospital commu- nications: A review of discharge summaries. Journal of quality in clinical practice, 21:104–8, 01 2002

2002
[44]

Biometric and mobile gait analysis for early diagnosis and therapy monitoring in parkinson’s disease

Jens Barth, Jochen Klucken, Patrick Kugler, Thomas Kammerer, Ralph Steidl, Jürgen Winkler, Joachim Horneg- ger, and Björn Eskoﬁer. Biometric and mobile gait analysis for early diagnosis and therapy monitoring in parkinson’s disease. In 2011 Annual International Conference of t...

2011
[45]

An adaptive deep learning approach for ppg-based identiﬁcation

Vasu Jindal, Javad Birjandtalab, M Baran Pouyan, and Mehrdad Nourani. An adaptive deep learning approach for ppg-based identiﬁcation. In 2016 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pages 6401–6404. IEEE, 2016

2016
[46]

Decoding eeg and lfp signals using deep learning: heading truenorth

Ewan Nurse, Benjamin S Mashford, Antonio Jimeno Yepes, Isabell Kiral-Kornek, Stefan Harrer, and Dean R Freestone. Decoding eeg and lfp signals using deep learning: heading truenorth. In Proceedings of the ACM International Conference on Computing Frontiers, pages 259–266. ACM, 2016

2016
[47]

Improving tuberculosis diagnostics using deep learning and mobile health technologies among resource-poor and marginalized communities

Yu Cao, Chang Liu, Benyuan Liu, Maria J Brunette, Ning Zhang, Tong Sun, Peifeng Zhang, Jesus Peinado, Epifanio Sanchez Garavito, Leonid Lecca Garcia, et al. Improving tuberculosis diagnostics using deep learning and mobile health technologies among resource-poor and marginaliz...

2016
[48]

Social restricted boltzmann machine: Human behavior prediction in health social networks

NhatHai Phan, Dejing Dou, Brigitte Piniewski, and David Kil. Social restricted boltzmann machine: Human behavior prediction in health social networks. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pages 42...

2015
[49]

Social media image analysis for public health

Venkata Rama Kiran Garimella, Abdulrahman Alfayad, and Ingmar Weber. Social media image analysis for public health. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 5543–5547. ACM, 2016

2016
[50]

On the ground validation of online diagnosis with twitter and medical records

Todd Bodnar, Victoria C Barclay, Nilam Ram, Conrad S Tucker, and Marcel Salathé. On the ground validation of online diagnosis with twitter and medical records. In Proceedings of the 23rd International Conference on World Wide Web, pages 651–656. ACM, 2014. 28 A PREPRINT - AUGU...

2014
[51]

An ensemble heterogeneous classiﬁcation methodology for discovering health-related knowledge in social media messages

Suppawong Tuarob, Conrad S Tucker, Marcel Salathe, and Nilam Ram. An ensemble heterogeneous classiﬁcation methodology for discovering health-related knowledge in social media messages. Journal of biomedical informatics, 49:255–268, 2014

2014
[52]

# hayfever; a longitudinal study into hay fever related tweets in the uk

Ed de Quincey, Theocharis Kyriacou, and Thomas Pantin. # hayfever; a longitudinal study into hay fever related tweets in the uk. In Proceedings of the 6th international conference on digital health conference, pages 85–89. ACM, 2016

2016
[53]

A machine learning approach to classiﬁcation of drug reviews in russian

Ilseyar Alimova, Elena Tutubalina, Julia Alferova, and Guzel Gaﬁyatullina. A machine learning approach to classiﬁcation of drug reviews in russian. In 2017 Ivannikov ISPRAS Open Conference (ISPRAS), pages 64–69, 11 2017

2017
[54]

Predicting infectious disease using deep learning and big data

Sangwon Chae, Sungjun Kwon, and Donghyun Lee. Predicting infectious disease using deep learning and big data. International journal of environmental research and public health, 15(8):1596, 2018

2018
[55]

Uses of electronic health records for public health surveillance to advance public health

Guthrie S Birkhead, Michael Klompas, and Nirav R Shah. Uses of electronic health records for public health surveillance to advance public health. Annual review of public health, 36:345–359, 2015

2015
[56]

Adoption of electronic health record systems among us non-federal acute care hospitals: 2008-2015

J Henry, Yuriy Pylypchuk, Talisha Searcy, and Vaishali Patel. Adoption of electronic health record systems among us non-federal acute care hospitals: 2008-2015. ONC Data Brief, 35:1–9, 2016

2008
[58]

Support vector machine soft margin classiﬁers: error analysis

Di-Rong Chen, Qiang Wu, Yiming Ying, and Ding-Xuan Zhou. Support vector machine soft margin classiﬁers: error analysis. Journal of Machine Learning Research, 5(Sep):1143–1175, 2004

2004
[59]

Support vector machines

John Shawe-Taylor and Nello Cristianini. Support vector machines. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, pages 93–112, 2000

2000
[60]

Multilinear operators for higher-order decompositions

Tamara Gibson Kolda. Multilinear operators for higher-order decompositions. Technical report, Sandia National Laboratories, 2006

2006
[61]

Word2vec — Wikipedia, the free encyclopedia

Wikipedia contributors. Word2vec — Wikipedia, the free encyclopedia. [Online; accessed 11-August-]
[63]

Learning representations by back-propagating errors

David E Rumelhart, Geoffrey E Hinton, Ronald J Williams, et al. Learning representations by back-propagating errors. Cognitive modeling, 5(3):1, 1988

1988
[65]

Imagenet classiﬁcation with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classiﬁcation with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012

2012
[66]

Receptive ﬁelds, binocular interaction and functional architecture in the cat’s visual cortex.The Journal of physiology, 160(1):106–154, 1962

David H Hubel and Torsten N Wiesel. Receptive ﬁelds, binocular interaction and functional architecture in the cat’s visual cortex.The Journal of physiology, 160(1):106–154, 1962

1962
[67]

3d convolutional neural networks for human action recognition

Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu. 3d convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1):221–231, 2012

2012
[68]

A learning algorithm for continually running fully recurrent neural networks

Ronald J Williams and David Zipser. A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2):270–280, 1989

1989
[69]

Learning phrase representations using rnn encoder-decoder for statistical machine translation

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014

2014 arXiv
[70]

Lstm: A search space odyssey

Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10):2222–2232, 2016

2016
[71]

Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel P. Kuksa. Natural language processing (almost) from scratch. CoRR, abs/1103.0398, 2011

2011 arXiv
[72]

Learning long-term dependencies with gradient descent is difﬁcult

Yoshua Bengio, Patrice Simard, Paolo Frasconi, et al. Learning long-term dependencies with gradient descent is difﬁcult. IEEE transactions on neural networks, 5(2):157–166, 1994

1994
[74]

Reducing the dimensionality of data with neural networks

Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks. science, 313(5786):504–507, 2006. 29 A PREPRINT - AUGUST 11, 2020

2006
[75]

Deep Learning

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www. deeplearningbook.org

2016
[76]

Autoencoder — Wikipedia, the free encyclopedia

Wikipedia contributors. Autoencoder — Wikipedia, the free encyclopedia. [Online; accessed 11-August-]
[77]

Efﬁcient learning of sparse representations with an energy-based model

Christopher Poultney, Sumit Chopra, Yann L Cun, et al. Efﬁcient learning of sparse representations with an energy-based model. In Advances in neural information processing systems, pages 1137–1144, 2007

2007
[78]

Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion

Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 11(Dec):3371–3408, 2010

2010
[79]

Extracting and composing robust features with denoising autoencoders

Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pages 1096–1103. ACM, 2008

2008
[80]

Contractive auto-encoders: Explicit invariance during feature extraction

Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio. Contractive auto-encoders: Explicit invariance during feature extraction. InProceedings of the 28th International Conference on International Conference on Machine Learning, pages 833–840. Omnipress, 2011

2011
[81]

Exploring strategies for training deep neural networks

Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, and Pascal Lamblin. Exploring strategies for training deep neural networks. Journal of machine learning research, 10(Jan):1–40, 2009

2009
[82]

A fast learning algorithm for deep belief nets

Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7):1527–1554, 2006

2006
[83]

Learning and relearning in boltzmann machines

Geoffrey E Hinton, Terrence J Sejnowski, et al. Learning and relearning in boltzmann machines. Parallel distributed processing: Explorations in the microstructure of cognition, 1(282-317):2, 1986

1986
[84]

Probabilistic reasoning in intelligent systems: networks of plausible inference

Judea Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier, 2014

2014
[85]

Deep boltzmann machines

Ruslan Salakhutdinov and Geoffrey Hinton. Deep boltzmann machines. In Artiﬁcial intelligence and statistics, pages 448–455, 2009

2009
[86]

Attention is all you need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998–6008, 2017

2017
[87]

Neural machine translation by jointly learning to align and translate

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014

2014 arXiv
[88]

How transferable are features in deep neural networks? In Advances in neural information processing systems, pages 3320–3328, 2014

Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In Advances in neural information processing systems, pages 3320–3328, 2014

2014
[89]

Learning many related tasks at the same time with backpropagation

Rich Caruana. Learning many related tasks at the same time with backpropagation. In Advances in neural information processing systems, pages 657–664, 1995

1995
[90]

Deep learning of representations for unsupervised and transfer learning

Yoshua Bengio. Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML workshop on unsupervised and transfer learning, pages 17–36, 2012

2012
[91]

Deep learners beneﬁt more from out-of-distribution examples

Yoshua Bengio, Frédéric Bastien, Arnaud Bergeron, Nicolas Boulanger-Lewandowski, Thomas Breuel, Youssouf Chherawala, Moustapha Cisse, Myriam Côté, Dumitru Erhan, Jeremy Eustache, et al. Deep learners beneﬁt more from out-of-distribution examples. In Proceedings of the Fourteen...

2011
[92]

Introduction to reinforcement learning, volume 135

Richard S Sutton, Andrew G Barto, et al. Introduction to reinforcement learning, volume 135. MIT press Cambridge, 1998

1998
[93]

Reinforcement learning: A survey

Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore. Reinforcement learning: A survey. Journal of artiﬁcial intelligence research, 4:237–285, 1996

1996
[94]

Multiple model-based reinforcement learning

Kenji Doya, Kazuyuki Samejima, Ken-ichi Katagiri, and Mitsuo Kawato. Multiple model-based reinforcement learning. Neural computation, 14(6):1347–1369, 2002

2002
[95]

A survey of actor-critic reinforcement learning: Standard and natural policy gradients

Ivo Grondman, Lucian Busoniu, Gabriel AD Lopes, and Robert Babuska. A survey of actor-critic reinforcement learning: Standard and natural policy gradients. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6):1291–1307, 2012

2012
[96]

Representation learning: A review and new perspectives

Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8):1798–1828, 2013

2013
[97]

Learning continuous control policies by stochastic value gradients

Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, and Yuval Tassa. Learning continuous control policies by stochastic value gradients. In Advances in Neural Information Processing Systems, pages 2944–2952, 2015. 30 A PREPRINT - AUGUST 11, 2020

2015
[98]

Gradient estimation using stochastic computation graphs

John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel. Gradient estimation using stochastic computation graphs. In Advances in Neural Information Processing Systems, pages 3528–3536, 2015

2015
[99]

A brief survey of deep reinforcement learning

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. A brief survey of deep reinforcement learning. arXiv preprint arXiv:1708.05866, 2017

2017 arXiv
[100]

Predicting alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks

Adrien Payan and Giovanni Montana. Predicting alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks. arXiv preprint arXiv:1502.02506, 2015

2015 arXiv
[101]

Going deeper with convolutions

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9, 2015

2015
[102]

Deep residual learning for image recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, abs/1512.03385, 2015

2015 arXiv
[103]

Very deep convolutional networks for large-scale image recognition

Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014

2014 arXiv
[104]

Aggregated residual transformations for deep neural networks

Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1492–1500, 2017

2017
[105]

Feature pyramid networks for object detection

Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117–2125, 2017

2017
[106]

Anatomy-speciﬁc classiﬁcation of medical images using deep convolutional nets

Holger R Roth, Christopher T Lee, Hoo-Chang Shin, Ari Seff, Lauren Kim, Jianhua Yao, Le Lu, and Ronald M Summers. Anatomy-speciﬁc classiﬁcation of medical images using deep convolutional nets. arXiv preprint arXiv:1504.04003, 2015

2015 arXiv
[107]

Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images

Mark JJP Van Grinsven, Bram van Ginneken, Carel B Hoyng, Thomas Theelen, and Clara I Sánchez. Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE transactions on medical imaging, 35(5):1273–...

2016
[108]

Lung pattern classiﬁcation for interstitial lung diseases using a deep convolutional neural network

Marios Anthimopoulos, Stergios Christodoulidis, Lukas Ebner, Andreas Christe, and Stavroula Mougiakakou. Lung pattern classiﬁcation for interstitial lung diseases using a deep convolutional neural network. IEEE transactions on medical imaging, 35(5):1207–1216, 2016

2016
[109]

Automated lung nodule detection and classiﬁcation based on multiple classiﬁers voting.Microscopy research and technique, 82(9):1601–1609, 2019

Tanzila Saba. Automated lung nodule detection and classiﬁcation based on multiple classiﬁers voting.Microscopy research and technique, 82(9):1601–1609, 2019

2019
[110]

Dermatologist-level classiﬁcation of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun. Dermatologist-level classiﬁcation of skin cancer with deep neural networks. Nature, 542(7639):115, 2017

2017
[111]

A practical guide to training restricted boltzmann machines

Geoffrey E Hinton. A practical guide to training restricted boltzmann machines. In Neural networks: Tricks of the trade, pages 599–619. Springer, 2012

2012
[112]

Auto-encoding variational bayes

Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013

2013 arXiv
[113]

Manifold learning of brain mris by deep learning

Tom Brosch, Roger Tam, Alzheimer’s Disease Neuroimaging Initiative, et al. Manifold learning of brain mris by deep learning. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 633–640. Springer, 2013

2013
[114]

Deep learning for neuroimaging: a validation study

Sergey M Plis, Devon R Hjelm, Ruslan Salakhutdinov, Elena A Allen, Henry J Bockholt, Jeffrey D Long, Hans J Johnson, Jane S Paulsen, Jessica A Turner, and Vince D Calhoun. Deep learning for neuroimaging: a validation study. Frontiers in neuroscience, 8:229, 2014

2014
[115]

Deep learning-based feature representation for ad/mci classiﬁcation

Heung-Il Suk and Dinggang Shen. Deep learning-based feature representation for ad/mci classiﬁcation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 583–590. Springer, 2013

2013
[116]

Hier- archical feature representation and multimodal fusion with deep learning for ad/mci diagnosis

Heung-Il Suk, Seong-Whan Lee, Dinggang Shen, Alzheimer’s Disease Neuroimaging Initiative, et al. Hier- archical feature representation and multimodal fusion with deep learning for ad/mci diagnosis. NeuroImage, 101:569–582, 2014

2014
[117]

Combining generative and discriminative representation learning for lung ct analysis with convolutional restricted boltzmann machines

Gijs van Tulder and Marleen de Bruijne. Combining generative and discriminative representation learning for lung ct analysis with convolutional restricted boltzmann machines. IEEE transactions on medical imaging , 35(5):1262–1272, 2016

2016
[118]

Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans

Jie-Zhi Cheng, Dong Ni, Yi-Hong Chou, Jing Qin, Chui-Mei Tiu, Yeun-Chung Chang, Chiun-Sheng Huang, Dinggang Shen, and Chung-Ming Chen. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans. Scien...

2016
[119]

A deep learning method for microaneurysm detection in fundus images

Juan Shan and Lin Li. A deep learning method for microaneurysm detection in fundus images. In 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pages 357–358. IEEE, 2016

2016
[120]

Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning

Hoo-Chang Shin, Holger R Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, and Ronald M Summers. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transaction...

2016
[121]

Standard plane localization in fetal ultrasound via domain transferred deep neural networks

Hao Chen, Dong Ni, Jing Qin, Shengli Li, Xin Yang, Tianfu Wang, and Pheng Ann Heng. Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE journal of biomedical and health informatics, 19(5):1627–1636, 2015

2015
[122]

Convolutional neural networks for medical image analysis: Full training or ﬁne tuning? IEEE transactions on medical imaging, 35(5):1299–1312, 2016

Nima Tajbakhsh, Jae Y Shin, Suryakanth R Gurudu, R Todd Hurst, Christopher B Kendall, Michael B Gotway, and Jianming Liang. Convolutional neural networks for medical image analysis: Full training or ﬁne tuning? IEEE transactions on medical imaging, 35(5):1299–1312, 2016

2016
[123]

Transfer learning of a convolutional neural network for hep-2 cell image classiﬁcation

Ha Tran Hong Phan, Ashnil Kumar, Jinman Kim, and Dagan Feng. Transfer learning of a convolutional neural network for hep-2 cell image classiﬁcation. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pages 1208–1211. IEEE, 2016

2016
[124]

Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, Syoko Ueno, Takeshi Kubo, Tomohiro Kuroda, and Kaori Togashi. Computer-aided diagnosis of lung nodule classiﬁcation between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolu...

2018
[125]

3-d convolutional encoder-decoder network for low-dose ct via transfer learning from a 2-d trained network

Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K Kalra, Ling Sun, Wenxiang Cong, and Ge Wang. 3-d convolutional encoder-decoder network for low-dose ct via transfer learning from a 2-d trained network. IEEE transactions on medical imaging, 37(6):1522–1534, 2018

2018
[126]

Alzheimer’s disease diagnostics by a deeply supervised adaptable 3d convolutional network

Ehsan Hosseini-Asl, Georgy Gimel’farb, and Ayman El-Baz. Alzheimer’s disease diagnostics by a deeply supervised adaptable 3d convolutional network. arXiv preprint arXiv:1607.00556, 2016

2016 arXiv
[127]

Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classiﬁcation

Wentao Zhu, Chaochun Liu, Wei Fan, and Xiaohui Xie. Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classiﬁcation. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 673–681. IEEE, 2018

2018
[128]

Efﬁcient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation

Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, and Ben Glocker. Efﬁcient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis, 36:61–78, 2017

2017
[129]

Multi-scale convolutional neural networks for lung nodule classiﬁcation

Wei Shen, Mu Zhou, Feng Yang, Caiyun Yang, and Jie Tian. Multi-scale convolutional neural networks for lung nodule classiﬁcation. In International Conference on Information Processing in Medical Imaging, pages 588–599. Springer, 2015

2015
[130]

Automatic segmentation of mr brain images with a convolutional neural network

Pim Moeskops, Max A Viergever, Adriënne M Mendrik, Linda S de Vries, Manon JNL Benders, and Ivana Išgum. Automatic segmentation of mr brain images with a convolutional neural network. IEEE transactions on medical imaging, 35(5):1252–1261, 2016

2016
[131]

Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning

Youyi Song, Ling Zhang, Siping Chen, Dong Ni, Baiying Lei, and Tianfu Wang. Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning. IEEE Transactions on Biomedical Engineering, 62(10):2421–2433, 2015

2015
[132]

Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain

Wei Yang, Yingyin Chen, Yunbi Liu, Liming Zhong, Genggeng Qin, Zhentai Lu, Qianjin Feng, and Wufan Chen. Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Medical image analysis, 35:421–433, 2017

2017
[133]

Active appearance model and deep learning for more accurate prostate segmentation on mri

Ruida Cheng, Holger R Roth, Le Lu, Shijun Wang, Baris Turkbey, William Gandler, Evan S McCreedy, Harsh K Agarwal, Peter Choyke, Ronald M Summers, et al. Active appearance model and deep learning for more accurate prostate segmentation on mri. In Medical Imaging 2016: Image Pro...

2016
[134]

Automated anatomical landmark detection ondistal femur surface using convolutional neural network

Dong Yang, Shaoting Zhang, Zhennan Yan, Chaowei Tan, Kang Li, and Dimitris Metaxas. Automated anatomical landmark detection ondistal femur surface using convolutional neural network. In 2015 IEEE 12th international symposium on biomedical imaging (ISBI), pages 17–21. IEEE, 2015

2015
[135]

Multi-resolution-tract cnn with hybrid pretrained and skin-lesion trained layers

Jeremy Kawahara and Ghassan Hamarneh. Multi-resolution-tract cnn with hybrid pretrained and skin-lesion trained layers. In International Workshop on Machine Learning in Medical Imaging, pages 164–171. Springer, 2016. 32 A PREPRINT - AUGUST 11, 2020

2016
[136]

Improving computer-aided detection using convolutional neural networks and random view aggregation

Holger R Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, and Ronald M Summers. Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE transactions on medical imaging, 35(5):1170–1181, 2015

2015
[137]

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

Francesco Ciompi, Kaman Chung, Sarah J Van Riel, Arnaud Arindra Adiyoso Setio, Paul K Gerke, Colin Jacobs, Ernst Th Scholten, Cornelia Schaefer-Prokop, Mathilde MW Wille, Alfonso Marchiano, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep...

2017
[138]

Multi-scale deep reinforcement learning for real-time 3d-landmark detection in ct scans

Florin-Cristian Ghesu, Bogdan Georgescu, Yefeng Zheng, Sasa Grbic, Andreas Maier, Joachim Hornegger, and Dorin Comaniciu. Multi-scale deep reinforcement learning for real-time 3d-landmark detection in ct scans. IEEE transactions on pattern analysis and machine intelligence, 41...

2017
[139]

Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection

Amir Alansary, Ozan Oktay, Yuanwei Li, Loic Le Folgoc, Benjamin Hou, Ghislain Vaillant, Konstantinos Kamnitsas, Athanasios Vlontzos, Ben Glocker, Bernhard Kainz, and Daniel Rueckert. Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection. Medical Image Anal...

2019
[140]

Learning to detect chest radiographs containing lung nodules using visual attention networks

Emanuele Pesce, Petros-Pavlos Ypsilantis, Samuel Withey, Robert Bakewell, Vicky Goh, and Giovanni Montana. Learning to detect chest radiographs containing lung nodules using visual attention networks. arXiv preprint arXiv:1712.00996, 2017

2017 arXiv
[141]

A robust deep model for improved classiﬁcation of ad/mci patients

Feng Li, Loc Tran, Kim-Han Thung, Shuiwang Ji, Dinggang Shen, and Jiang Li. A robust deep model for improved classiﬁcation of ad/mci patients. IEEE journal of biomedical and health informatics, 19(5):1610–1616, 2015

2015
[142]

3d deep learning for efﬁcient and robust landmark detection in volumetric data

Yefeng Zheng, David Liu, Bogdan Georgescu, Hien Nguyen, and Dorin Comaniciu. 3d deep learning for efﬁcient and robust landmark detection in volumetric data. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 565–572. Springer, 2015

2015
[143]

Diagnosis of alzheimer’s disease via multi-modality 3d convolutional neural network

Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, Alzheimer’s Disease Neuroimag- ing Initiative (ADNI, et al. Diagnosis of alzheimer’s disease via multi-modality 3d convolutional neural network. Frontiers in Neuroscience, 13:509, 2019

2019
[144]

Automated detection of pulmonary nodules in pet/ct images: Ensemble false-positive reduction using a convolutional neural network technique

Atsushi Teramoto, Hiroshi Fujita, Osamu Yamamuro, and Tsuneo Tamaki. Automated detection of pulmonary nodules in pet/ct images: Ensemble false-positive reduction using a convolutional neural network technique. Medical physics, 43(6Part1):2821–2827, 2016

2016
[145]

Fully convolutional networks for semantic segmentation

Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431–3440, 2015

2015
[146]

U-net: Convolutional networks for biomedical image segmentation

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015

2015
[147]

V-net: Fully convolutional neural networks for volumetric medical image segmentation

Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 Fourth International Conference on 3D Vision (3DV), pages 565–571. IEEE, 2016

2016
[148]

3d u-net: learning dense volumetric segmentation from sparse annotation

Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger. 3d u-net: learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention, pages 424–432. Springer, 2016

2016
[149]

The importance of skip connections in biomedical image segmentation

Michal Drozdzal, Eugene V orontsov, Gabriel Chartrand, Samuel Kadoury, and Chris Pal. The importance of skip connections in biomedical image segmentation. In Deep Learning and Data Labeling for Medical Applications, pages 179–187. Springer, 2016

2016
[150]

Segnet: A deep convolutional encoder-decoder archi- tecture for image segmentation

Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder archi- tecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12):2481– 2495, 2017

2017
[151]

Anatomynet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy

Wentao Zhu, Yufang Huang, Liang Zeng, Xuming Chen, Yong Liu, Zhen Qian, Nan Du, Wei Fan, and Xiaohui Xie. Anatomynet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical physics, 46(2):576–589, 2019

2019
[152]

A modiﬁed u-net convolutional network featuring a nearest-neighbor re-sampling-based elastic-transformation for brain tissue characterization and segmentation

SM Kamrul Hasan and Cristian A Linte. A modiﬁed u-net convolutional network featuring a nearest-neighbor re-sampling-based elastic-transformation for brain tissue characterization and segmentation. In 2018 IEEE Western New York Image and Signal Processing Workshop (WNYISPW), p...

2018
[153]

Segmentation of retinal ﬂuid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images

Meng-Xiao Li, Su-Qin Yu, Wei Zhang, Hao Zhou, Xun Xu, Tian-Wei Qian, and Yong-Jing Wan. Segmentation of retinal ﬂuid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images. International journal of op...

2019
[154]

A multi-scale u-net for semantic segmentation of histological images from radical prostatectomies

Jiayun Li, Karthik V Sarma, King Chung Ho, Arkadiusz Gertych, Beatrice S Knudsen, and Corey W Arnold. A multi-scale u-net for semantic segmentation of histological images from radical prostatectomies. In AMIA Annual Symposium Proceedings, volume 2017, page 1140. American Medic...

2017
[155]

Spatial clockwork recurrent neural network for muscle perimysium segmentation

Yuanpu Xie, Zizhao Zhang, Manish Sapkota, and Lin Yang. Spatial clockwork recurrent neural network for muscle perimysium segmentation. In International Conference on Medical Image Computing and Computer- Assisted Intervention, pages 185–193. Springer, 2016

2016
[156]

Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation

Marijn F Stollenga, Wonmin Byeon, Marcus Liwicki, and Juergen Schmidhuber. Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation. In Advances in neural information processing systems, pages 2998–3006, 2015

2015
[157]

Multi-dimensional gated recurrent units for the segmen- tation of biomedical 3d-data

Simon Andermatt, Simon Pezold, and Philippe Cattin. Multi-dimensional gated recurrent units for the segmen- tation of biomedical 3d-data. In Deep Learning and Data Labeling for Medical Applications, pages 142–151. Springer, 2016

2016
[158]

Combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation

Jianxu Chen, Lin Yang, Yizhe Zhang, Mark Alber, and Danny Z Chen. Combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation. In Advances in neural information processing systems, pages 3036–3044, 2016

2016
[159]

Recognizing end-diastole and end- systole frames via deep temporal regression network

Bin Kong, Yiqiang Zhan, Min Shin, Thomas Denny, and Shaoting Zhang. Recognizing end-diastole and end- systole frames via deep temporal regression network. In International conference on medical image computing and computer-assisted intervention, pages 264–272. Springer, 2016

2016
[160]

Breast cancer classiﬁcation from histopathological images with inception recurrent residual convolutional neural network

Md Zahangir Alom, Chris Yakopcic, Mst Shamima Nasrin, Tarek M Taha, and Vijayan K Asari. Breast cancer classiﬁcation from histopathological images with inception recurrent residual convolutional neural network. Journal of digital imaging, pages 1–13, 2019

2019
[161]

Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation

Rudra PK Poudel, Pablo Lamata, and Giovanni Montana. Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation. In Reconstruction, segmentation, and analysis of medical images, pages 83–94. Springer, 2016

2016
[162]

V olumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images

Lequan Yu, Xin Yang, Hao Chen, Jing Qin, and Pheng Ann Heng. V olumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images. In Thirty-ﬁrst AAAI conference on artiﬁcial intelligence, 2017

2017
[163]

Fully convolutional structured lstm networks for joint 4d medical image segmentation

Yang Gao, Jeff M Phillips, Yan Zheng, Renqiang Min, P Thomas Fletcher, and Guido Gerig. Fully convolutional structured lstm networks for joint 4d medical image segmentation. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pages 1104–1108. IEEE, 2018

2018
[164]

Adversarial deep structured nets for mass segmentation from mammograms

Wentao Zhu, Xiang Xiang, Trac D Tran, Gregory D Hager, and Xiaohui Xie. Adversarial deep structured nets for mass segmentation from mammograms. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pages 847–850. IEEE, 2018

2018
[165]

Sub-cortical brain structure segmentation using f-cnn’s

Mahsa Shakeri, Stavros Tsogkas, Enzo Ferrante, Sarah Lippe, Samuel Kadoury, Nikos Paragios, and Iasonas Kokkinos. Sub-cortical brain structure segmentation using f-cnn’s. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pages 269–272. IEEE, 2016

2016
[166]

Fast fully automatic segmentation of the human placenta from motion corrupted mri

Amir Alansary, Konstantinos Kamnitsas, Alice Davidson, Rostislav Khlebnikov, Martin Rajchl, Christina Malamateniou, Mary Rutherford, Joseph V Hajnal, Ben Glocker, Daniel Rueckert, et al. Fast fully automatic segmentation of the human placenta from motion corrupted mri. In Inte...

2016
[167]

Multi-modal vertebrae recognition using transformed deep convolution network.Computerized medical imaging and graphics, 51:11–19, 2016

Yunliang Cai, Mark Landis, David T Laidley, Anat Kornecki, Andrea Lum, and Shuo Li. Multi-modal vertebrae recognition using transformed deep convolution network.Computerized medical imaging and graphics, 51:11–19, 2016

2016
[168]

Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random ﬁelds

Patrick Ferdinand Christ, Mohamed Ezzeldin A Elshaer, Florian Ettlinger, Sunil Tatavarty, Marc Bickel, Patrick Bilic, Markus Rempﬂer, Marco Armbruster, Felix Hofmann, Melvin D’Anastasi, et al. Automatic liver and lesion segmentation in ct using cascaded fully convolutional neu...

2016
[169]

Deepvessel: Retinal vessel segmentation via deep learning and conditional random ﬁeld

Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, and Jiang Liu. Deepvessel: Retinal vessel segmentation via deep learning and conditional random ﬁeld. In International conference on medical image computing and computer-assisted intervention, pages 132–139. Springer, 2016

2016
[170]

Summers, and Daniel J

Mingchen Gao, Ziyue Xu, Le Lu, Aaron Wu, Isabella Nogues, Ronald M. Summers, and Daniel J. Mollura. Segmentation label propagation using deep convolutional neural networks and dense conditional random ﬁeld. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), p...

2016
[171]

Multi-scale context aggregation by dilated convolutions

Fisher Yu and Vladlen Koltun. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122, 2015

2015 arXiv
[172]

Rethinking atrous convolution for semantic image segmentation

Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017

2017 arXiv
[173]

Deep attentive features for prostate segmentation in 3d transrectal ultrasound

Yi Wang, Haoran Dou, Xiaowei Hu, Lei Zhu, Xin Yang, Ming Xu, Jing Qin, Pheng-Ann Heng, Tianfu Wang, and Dong Ni. Deep attentive features for prostate segmentation in 3d transrectal ultrasound. IEEE transactions on medical imaging, 2019

2019
[174]

Ultrasound image segmentation: A deeply supervised network with attention to boundaries

Deepak Mishra, Santanu Chaudhury, Mukul Sarkar, and Arvinder Singh Soin. Ultrasound image segmentation: A deeply supervised network with attention to boundaries. IEEE Transactions on Biomedical Engineering , PP:1–1, 10 2018

2018
[175]

Deep 3d convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation

Tom Brosch, Lisa YW Tang, Youngjin Yoo, David KB Li, Anthony Traboulsee, and Roger Tam. Deep 3d convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE transactions on medical imaging, 35(5):1229–...

2016
[176]

Deep learning as a tool for increased accuracy and efﬁciency of histopathological diagnosis

Geert Litjens, Clara I Sánchez, Nadya Timofeeva, Meyke Hermsen, Iris Nagtegaal, Iringo Kovacs, Christina Hulsbergen-Van De Kaa, Peter Bult, Bram Van Ginneken, and Jeroen Van Der Laak. Deep learning as a tool for increased accuracy and efﬁciency of histopathological diagnosis. ...

2016
[177]

Brain tumor segmentation using convolutional neural networks in mri images

Sérgio Pereira, Adriano Pinto, Victor Alves, and Carlos A Silva. Brain tumor segmentation using convolutional neural networks in mri images. IEEE transactions on medical imaging, 35(5):1240–1251, 2016

2016
[178]

Unsupervised category discovery via looped deep pseudo-task optimization using a large scale radiology image database

Xiaosong Wang, Le Lu, Hoo-chang Shin, Lauren Kim, Isabella Nogues, Jianhua Yao, and Ronald Summers. Unsupervised category discovery via looped deep pseudo-task optimization using a large scale radiology image database. arXiv preprint arXiv:1603.07965, 2016

2016 arXiv
[179]

Interleaved text/image deep mining on a very large-scale radiology database

Hoo-Chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, and Ronald M Summers. Interleaved text/image deep mining on a very large-scale radiology database. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1090–1099, 2015

2015
[180]

Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation

Hoo-Chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, and Ronald M Summers. Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 24...

2016
[181]

Medical image description using multi-task-loss cnn

Pavel Kisilev, Eli Sason, Ella Barkan, and Sharbell Hashoul. Medical image description using multi-task-loss cnn. In Deep Learning and Data Labeling for Medical Applications, pages 121–129. Springer, 2016

2016
[182]

Deep visual-semantic alignments for generating image descriptions

Andrej Karpathy and Li Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3128–3137, 2015

2015
[183]

Statistical iterative cbct reconstruction based on neural network

Binbin Chen, Kai Xiang, Zaiwen Gong, Jing Wang, and Shan Tan. Statistical iterative cbct reconstruction based on neural network. IEEE transactions on medical imaging, 37(6):1511–1521, 2018

2018
[184]

An artiﬁcial agent for robust image registration

Rui Liao, Shun Miao, Pierre de Tournemire, Sasa Grbic, Ali Kamen, Tommaso Mansi, and Dorin Comaniciu. An artiﬁcial agent for robust image registration. In Thirty-First AAAI Conference on Artiﬁcial Intelligence, 2017

2017
[185]

Next-generation phenotyping of electronic health records

George Hripcsak and David J Albers. Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1):117–121, 2012

2012
[186]

Mining electronic health records: towards better research applications and clinical care

Peter B Jensen, Lars J Jensen, and Søren Brunak. Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics, 13(6):395, 2012

2012
[188]

A survey on bias and fairness in machine learning

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635, 2019

1908 arXiv
[189]

Key challenges for delivering clinical impact with artiﬁcial intelligence

Christopher J Kelly, Alan Karthikesalingam, Mustafa Suleyman, Greg Corrado, and Dominic King. Key challenges for delivering clinical impact with artiﬁcial intelligence. BMC medicine, 17(1):195, 2019

2019
[190]

Improving palliative care with deep learning

Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, and Nigam H Shah. Improving palliative care with deep learning. BMC medical informatics and decision making, 18(4):122, 2018

2018
[191]

Medical concept representation learning from electronic health records and its application on heart failure prediction

Edward Choi, Andy Schuetz, Walter F Stewart, and Jimeng Sun. Medical concept representation learning from electronic health records and its application on heart failure prediction. arXiv preprint arXiv:1602.03686, 2016

2016 arXiv
[192]

Deepr: a convolutional net for medical records

Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, and Svetha Venkatesh. Deepr: a convolutional net for medical records. IEEE journal of biomedical and health informatics, 21(1):22–30, 2016. 35 A PREPRINT - AUGUST 11, 2020

2016
[193]

Modeling healthcare quality via compact representations of electronic health records

Jelena Stojanovic, Djordje Gligorijevic, Vladan Radosavljevic, Nemanja Djuric, Mihajlo Grbovic, and Zoran Obradovic. Modeling healthcare quality via compact representations of electronic health records. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), ...

2017
[194]

Deep ehr: Chronic disease prediction using medical notes

Jingshu Liu, Zachariah Zhang, and Narges Razavian. Deep ehr: Chronic disease prediction using medical notes. arXiv preprint arXiv:1808.04928, 2018

2018 arXiv
[195]

Deepcare: A deep dynamic memory model for predictive medicine

Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh. Deepcare: A deep dynamic memory model for predictive medicine. In Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining, pages 30–41. Springer, 2016

2016
[196]

Readmission prediction via deep contextual embedding of clinical concepts

Cao Xiao, Tengfei Ma, Adji B Dieng, David M Blei, and Fei Wang. Readmission prediction via deep contextual embedding of clinical concepts. PloS one, 13(4):e0195024, 2018

2018
[197]

Using machine learning approaches for emergency room visit prediction based on electronic health record data

Zhi Qiao, Ning Sun, Xiang Li, Eryu Xia, Shiwan Zhao, and Yong Qin. Using machine learning approaches for emergency room visit prediction based on electronic health record data. Studies in health technology and informatics, 247:111–115, 01 2018

2018
[198]

Predicting sequences of clinical events by using a personalized temporal latent embedding model

Cristóbal Esteban, Danilo Schmidt, Denis Krompaß, and V olker Tresp. Predicting sequences of clinical events by using a personalized temporal latent embedding model. In 2015 International Conference on Healthcare Informatics, pages 130–139. IEEE, 2015

2015
[199]

Predicting clinical events by combining static and dynamic information using recurrent neural networks

Cristóbal Esteban, Oliver Staeck, Stephan Baier, Yinchong Yang, and V olker Tresp. Predicting clinical events by combining static and dynamic information using recurrent neural networks. In 2016 IEEE International Conference on Healthcare Informatics (ICHI), pages 93–101. IEEE, 2016

2016
[200]

A hybrid neural network model for joint prediction of presence and period assertions of medical events in clinical notes

Li Rumeng, N Jagannatha Abhyuday, and Yu Hong. A hybrid neural network model for joint prediction of presence and period assertions of medical events in clinical notes. In AMIA Annual Symposium Proceedings, volume 2017, page 1149. American Medical Informatics Association, 2017

2017
[201]

Disease prediction model based on bilstm and attention mechanism

Yang Yang, Xiangwei Zheng, and Cun Ji. Disease prediction model based on bilstm and attention mechanism. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1141–1148. IEEE, 2019

2019
[202]

An attention based deep learning model of clinical events in the intensive care unit

Deepak A Kaji, John R Zech, Jun S Kim, Samuel K Cho, Neha S Dangayach, Anthony B Costa, and Eric K Oermann. An attention based deep learning model of clinical events in the intensive care unit. PloS one, 14(2):e0211057, 2019

2019
[203]

Can machine-learning improve cardiovascular risk prediction using routine clinical data? PloS one, 12(4):e0174944, 2017

Stephen F Weng, Jenna Reps, Joe Kai, Jonathan M Garibaldi, and Nadeem Qureshi. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PloS one, 12(4):e0174944, 2017

2017
[204]

Interpretable deep models for icu outcome prediction

Zhengping Che, Sanjay Purushotham, Robinder Khemani, and Yan Liu. Interpretable deep models for icu outcome prediction. In AMIA Annual Symposium Proceedings, volume 2016, page 371. American Medical Informatics Association, 2016

2016
[205]

Mortality prediction in intensive care units (icus) using a deep rule-based fuzzy classiﬁer

Raheleh Davoodi and Mohammad Hassan Moradi. Mortality prediction in intensive care units (icus) using a deep rule-based fuzzy classiﬁer. Journal of biomedical informatics, 79:48–59, 2018

2018
[206]

A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data

Sara Bersche Golas, Takuma Shibahara, Stephen Agboola, Hiroko Otaki, Jumpei Sato, Tatsuya Nakae, Toru Hisamitsu, Go Kojima, Jennifer Felsted, Sujay Kakarmath, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospec...

2018
[207]

Recurrent neural networks for multivariate time series with missing values

Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. Recurrent neural networks for multivariate time series with missing values. Scientiﬁc reports, 8(1):6085, 2018

2018
[208]

Semi-supervised learning of the electronic health record for phenotype stratiﬁcation

Brett K Beaulieu-Jones, Casey S Greene, et al. Semi-supervised learning of the electronic health record for phenotype stratiﬁcation. Journal of biomedical informatics, 64:168–178, 2016

2016
[209]

Deep patient: an unsupervised representation to predict the future of patients from the electronic health records

Riccardo Miotto, Li Li, Brian A Kidd, and Joel T Dudley. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientiﬁc reports, 6:26094, 2016

2016
[210]

Learning probabilistic phenotypes from heterogeneous ehr data

Rimma Pivovarov, Adler J Perotte, Edouard Grave, John Angiolillo, Chris H Wiggins, and Noémie Elhadad. Learning probabilistic phenotypes from heterogeneous ehr data. Journal of biomedical informatics, 58:156–165, 2015

2015
[211]

Latent dirichlet allocation

David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993–1022, 2003

2003
[212]

Warner, Joshua C

Juan Zhao, QiPing Feng, Patrick Wu, Jeremy L. Warner, Joshua C. Denny, and Wei-Qi Wei. Using topic modeling via non-negative matrix factorization to identify relationships between genetic variants and disease phenotypes: A case study of lipoprotein(a) (lpa). PLOS ONE, 14(2):1–...

2019
[213]

Bastarache, Robert J

Wei-Qi Wei, Lisa A. Bastarache, Robert J. Carroll, Joy E. Marlo, Travis J. Osterman, Eric R. Gamazon, Nancy J. Cox, Dan M. Roden, and Joshua C. Denny. Evaluating phecodes, clinical classiﬁcation software, and icd-9-cm codes for phenome-wide association studies in the electroni...

2017
[214]

Phenotyping through semi-supervised tensor factorization (psst)

Jette Henderson, Huan He, Bradley A Malin, Joshua C Denny, Abel N Kho, Joydeep Ghosh, and Joyce C Ho. Phenotyping through semi-supervised tensor factorization (psst). In AMIA Annual Symposium Proceedings, volume 2018, page 564. American Medical Informatics Association, 2018

2018
[215]

Identiﬁcation of asthma subtypes using clustering methodologies

Matea Deliu, Matthew Sperrin, Danielle Belgrave, and Adnan Custovic. Identiﬁcation of asthma subtypes using clustering methodologies. Pulmonary therapy, 2(1):19–41, 2016

2016
[216]

Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis

Christopher W Seymour, Jason N Kennedy, Shu Wang, Chung-Chou H Chang, Corrine F Elliott, Zhongying Xu, Scott Berry, Gilles Clermont, Gregory Cooper, Hernando Gomez, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. Jam...

2003
[217]

Cluster analysis to identify possible subgroups in tinnitus patients

Minke JC van den Berge, Rolien H Free, Rosemarie Arnold, Emile de Kleine, Rutger Hofman, J Marc C van Dijk, and Pim van Dijk. Cluster analysis to identify possible subgroups in tinnitus patients. Frontiers in neurology, 8:115, 2017

2017
[218]

Novel subgroups of attention-deﬁcit/hyperactivity dis- order identiﬁed by topological data analysis and their functional network modular organizations

Sunghyon Kyeong, Jae-Jin Kim, and Eunjoo Kim. Novel subgroups of attention-deﬁcit/hyperactivity dis- order identiﬁed by topological data analysis and their functional network modular organizations. PloS one, 12(8):e0182603, 2017

2017
[219]

Surrogate-assisted feature extraction for high-throughput phenotyping

Sheng Yu, Abhishek Chakrabortty, Katherine P Liao, Tianrun Cai, Ashwin N Ananthakrishnan, Vivian S Gainer, Susanne E Churchill, Peter Szolovits, Shawn N Murphy, Isaac S Kohane, et al. Surrogate-assisted feature extraction for high-throughput phenotyping. Journal of the America...

2016
[220]

Mandl, Isaac Kohane, Tianxi Cai, and Sheng Yu

Wenxin Ning, Stephanie Chan, Andrew Beam, Ming Yu, Alon Geva, Katherine Liao, Mary Mullen, Kenneth D. Mandl, Isaac Kohane, Tianxi Cai, and Sheng Yu. Feature extraction for phenotyping from semantic and knowledge resources. Journal of Biomedical Informatics, 91:103122, 2019

2019
[221]

Risk prediction with electronic health records: A deep learning approach

Yu Cheng, Fei Wang, Ping Zhang, and Jianying Hu. Risk prediction with electronic health records: A deep learning approach. In Proceedings of the 2016 SIAM International Conference on Data Mining, pages 432–440. SIAM, 2016

2016
[222]

Learning to diagnose with lstm recurrent neural networks

Zachary C Lipton, David C Kale, Charles Elkan, and Randall Wetzel. Learning to diagnose with lstm recurrent neural networks. arXiv preprint arXiv:1511.03677, 2015

2015 arXiv
[223]

Deep computational phenotyping

Zhengping Che, David Kale, Wenzhe Li, Mohammad Taha Bahadori, and Yan Liu. Deep computational phenotyping. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 507–516. ACM, 2015

2015
[224]

Structured prediction models for rnn based sequence labeling in clinical text

Abhyuday N Jagannatha and Hong Yu. Structured prediction models for rnn based sequence labeling in clinical text. In Proceedings of the conference on empirical methods in natural language processing. conference on empirical methods in natural language processing, volume 2016, ...

2016
[225]

Named entity recognition in chinese clinical text using deep neural network

Yonghui Wu, Min Jiang, Jianbo Lei, and Hua Xu. Named entity recognition in chinese clinical text using deep neural network. Studies in health technology and informatics, 216:624, 2015

2015
[226]

Deep learning for automated extraction of primary sites from cancer pathology reports

John X Qiu, Hong-Jun Yoon, Paul A Fearn, and Georgia D Tourassi. Deep learning for automated extraction of primary sites from cancer pathology reports. IEEE journal of biomedical and health informatics, 22(1):244–251, 2017

2017
[227]

Subgraph augmented non-negative tensor factorization (santf) for modeling clinical narrative text

Yuan Luo, Yu Xin, Ephraim Hochberg, Rohit Joshi, Ozlem Uzuner, and Peter Szolovits. Subgraph augmented non-negative tensor factorization (santf) for modeling clinical narrative text. Journal of the American Medical Informatics Association, 22(5):1009–1019, 2015

2015
[228]

Assigning clinical codes with data-driven concept representation on dutch clinical free text

Elyne Scheurwegs, Kim Luyckx, Léon Luyten, Bart Goethals, and Walter Daelemans. Assigning clinical codes with data-driven concept representation on dutch clinical free text. Journal of biomedical informatics, 69:118–127, 2017

2017
[229]

Improved pattern learning for bootstrapped entity extraction

Sonal Gupta and Christopher Manning. Improved pattern learning for bootstrapped entity extraction. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning, pages 98–108, 2014

2014
[230]

Brundleﬂy at semeval-2016 task 12: Recurrent neural networks vs

Jason Alan Fries. Brundleﬂy at semeval-2016 task 12: Recurrent neural networks vs. joint inference for clinical temporal information extraction. In SemEval@NAACL-HLT, 2016

2016
[231]

Deepdive: a data management system for automatic knowledge base construction

Ce Zhang. Deepdive: a data management system for automatic knowledge base construction. University of Wisconsin-Madison, Madison, Wisconsin, 2015. 37 A PREPRINT - AUGUST 11, 2020

2015
[232]

Clinical relation extraction with deep learning

Xinbo Lv, Yi Guan, Jinfeng Yang, and Jiawei Wu. Clinical relation extraction with deep learning. International Journal of Hybrid Information Technology, 9(7):237–248, 2016

2016
[233]

Diagnostic inferencing via improving clinical concept extraction with deep reinforcement learning: A preliminary study

Yuan Ling, Sadid A Hasan, Vivek Datla, Ashequl Qadir, Kathy Lee, Joey Liu, and Oladimeji Farri. Diagnostic inferencing via improving clinical concept extraction with deep reinforcement learning: A preliminary study. In Machine Learning for Healthcare Conference, pages 271–285, 2017

2017
[234]

Extraction of information related to adverse drug events from electronic health record notes: design of an end-to-end model based on deep learning

Fei Li, Weisong Liu, and Hong Yu. Extraction of information related to adverse drug events from electronic health record notes: design of an end-to-end model based on deep learning. JMIR medical informatics, 6(4):e12159, 2018

2018
[235]

Clinical relation extraction toward drug safety surveillance using electronic health record narratives: classical learning versus deep learning

Tsendsuren Munkhdalai, Feifan Liu, and Hong Yu. Clinical relation extraction toward drug safety surveillance using electronic health record narratives: classical learning versus deep learning. JMIR public health and surveillance, 4(2):e29, 2018

2018
[236]

Attention-based deep residual learning network for entity relation extraction in chinese emrs

Zhichang Zhang, Tong Zhou, Yu Zhang, and Yali Pang. Attention-based deep residual learning network for entity relation extraction in chinese emrs. BMC medical informatics and decision making, 19(2):55, 2019

2019
[237]

Learning low-dimensional representations of medical concepts

Youngduck Choi, Chill Yi-I Chiu, and David Sontag. Learning low-dimensional representations of medical concepts. AMIA Summits on Translational Science Proceedings, 2016:41, 2016

2016
[238]

Multi-layer representation learning for medical concepts

Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, Michael Thompson, James Bost, Javier Tejedor-Sojo, and Jimeng Sun. Multi-layer representation learning for medical concepts. InProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Dis...

2016
[239]

Using recurrent neural network models for early detection of heart failure onset

Edward Choi, Andy Schuetz, Walter F Stewart, and Jimeng Sun. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2):361–370, 2016

2016
[240]

Ensembles of randomized trees using diverse distributed representations of clinical events

Aron Henriksson, Jing Zhao, Hercules Dalianis, and Henrik Boström. Ensembles of randomized trees using diverse distributed representations of clinical events. BMC medical informatics and decision making, 16(2):69, 2016

2016
[241]

Learning vector representation of medical objects via emr-driven nonnegative restricted boltzmann machines (enrbm)

Truyen Tran, Tu Dinh Nguyen, Dinh Phung, and Svetha Venkatesh. Learning vector representation of medical objects via emr-driven nonnegative restricted boltzmann machines (enrbm). Journal of biomedical informatics, 54:96–105, 2015

2015
[242]

Doctor ai: Predicting clinical events via recurrent neural networks

Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F Stewart, and Jimeng Sun. Doctor ai: Predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference, pages 301–318, 2016

2016
[243]

Optimizing autoencoders for learning deep representations from health data

Chongyu Zhou, Jia Yao, and Mehul Motani. Optimizing autoencoders for learning deep representations from health data. IEEE Journal of Biomedical and Health Informatics, PP:1–1, 07 2018

2018
[244]

Temporal pattern and association discovery of diagnosis codes using deep learning

Saaed Mehrabi, Sunghwan Sohn, Dingheng Li, Joshua J Pankratz, Terry Therneau, Jennifer L St Sauver, Hongfang Liu, and Mathew Palakal. Temporal pattern and association discovery of diagnosis codes using deep learning. In 2015 International Conference on Healthcare Informatics, ...

2015
[245]

Deep patient similarity learning for personalized healthcare

Qiuling Suo, Fenglong Ma, Ye Yuan, Mengdi Huai, Weida Zhong, Jing Gao, and Aidong Zhang. Deep patient similarity learning for personalized healthcare. IEEE transactions on nanobioscience, 17(3):219–227, 2018

2018
[246]

De-identiﬁcation in natural language processing

Veronika Vincze and Richard Farkas. De-identiﬁcation in natural language processing. 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1300–1303, 2014

2014
[247]

De-identiﬁcation of clinical narratives through writing complexity measures

Muqun Li, David Carrell, John Aberdeen, Lynette Hirschman, and Bradley A Malin. De-identiﬁcation of clinical narratives through writing complexity measures. International journal of medical informatics, 83(10):750–767, 2014

2014
[248]

De-identiﬁcation of patient notes with recurrent neural networks

Franck Dernoncourt, Ji Young Lee, Ozlem Uzuner, and Peter Szolovits. De-identiﬁcation of patient notes with recurrent neural networks. Journal of the American Medical Informatics Association, 24(3):596–606, 2017

2017
[249]

Ensemble-based methods to improve de-identiﬁcation of electronic health record narratives

Youngjun Kim, Paul Heider, and Stéphane Meystre. Ensemble-based methods to improve de-identiﬁcation of electronic health record narratives. In AMIA Annual Symposium Proceedings, volume 2018, page 663. American Medical Informatics Association, 2018

2018
[250]

Natural language generation for electronic health records

Scott Lee. Natural language generation for electronic health records. In npj Digital Medicine, 2018

2018
[251]

Deep reinforcement learning for sepsis treatment

Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo Celi, Peter Szolovits, and Marzyeh Ghassemi. Deep reinforcement learning for sepsis treatment. arXiv preprint arXiv:1711.09602, 2017

2017 arXiv
[252]

Gate: Graph-attention augmented temporal neural network for medication recommendation

Chenhao Su, Sheng Gao, and Si Li. Gate: Graph-attention augmented temporal neural network for medication recommendation. IEEE Access, 2020. 38 A PREPRINT - AUGUST 11, 2020

2020
[253]

Interpretable batch irl to extract clinician goals in icu hypotension management

Srivatsan Srinivasan and Finale Doshi-Velez. Interpretable batch irl to extract clinician goals in icu hypotension management. AMIA Summits on Translational Science Proceedings, 2020:636, 2020

2020
[254]

A reinforcement learning approach to weaning of mechanical ventilation in intensive care units

Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, and Barbara E Engelhardt. A reinforcement learning approach to weaning of mechanical ventilation in intensive care units. arXiv preprint arXiv:1704.06300, 2017

2017 arXiv
[255]

Strategies for pre-training graph neural networks

Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265, 2019

1905 arXiv
[256]

Disruption of an sf2/asf-dependent exonic splicing enhancer in smn2 causes spinal muscular atrophy in the absence of smn1

Luca Cartegni and Adrian R Krainer. Disruption of an sf2/asf-dependent exonic splicing enhancer in smn2 causes spinal muscular atrophy in the absence of smn1. Nature genetics, 30(4):377, 2002

2002
[257]

Systems biology approaches and applications in obesity, diabetes, and cardiovascular diseases

Qingying Meng, Ville-Petteri Mäkinen, Helen Luk, and Xia Yang. Systems biology approaches and applications in obesity, diabetes, and cardiovascular diseases. Current cardiovascular risk reports, 7(1):73–83, 2013

2013
[258]

A deep learning framework for modeling structural features of rna-binding protein targets

Sai Zhang, Jingtian Zhou, Hailin Hu, Haipeng Gong, Ligong Chen, Chao Cheng, and Jianyang Zeng. A deep learning framework for modeling structural features of rna-binding protein targets. Nucleic acids research, 44(4):e32–e32, 2015

2015
[259]

Molecular graph convolutions: moving beyond ﬁngerprints

Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley. Molecular graph convolutions: moving beyond ﬁngerprints. Journal of computer-aided molecular design, 30(8):595–608, 2016

2016
[260]

Dann: a deep learning approach for annotating the pathogenicity of genetic variants

Daniel Quang, Yifei Chen, and Xiaohui Xie. Dann: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics, 31(5):761–763, 2014

2014
[261]

Bustamante, Xin Ma, and Wing H

Qigang Li, Keyan Zhao, Carlos D. Bustamante, Xin Ma, and Wing H. Wong. Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis. Genetics in Medicine, 01 2019

2019
[262]

Multi-level gene/mirna feature selection using deep belief nets and active learning

Rania Ibrahim, Noha A Yousri, Mohamed A Ismail, and Nagwa M El-Makky. Multi-level gene/mirna feature selection using deep belief nets and active learning. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 3957–3960. IEEE, 2014

2014
[263]

circdeep: Deep learning approach for circular rna classiﬁcation from other long non-coding rna

Mohamed Chaabane, Robert M Williams, Austin T Stephens, and Juw Won Park. circdeep: Deep learning approach for circular rna classiﬁcation from other long non-coding rna. Bioinformatics, 2019

2019
[264]

Convolutional neural network architectures for predicting dna–protein binding

Haoyang Zeng, Matthew D Edwards, Ge Liu, and David K Gifford. Convolutional neural network architectures for predicting dna–protein binding. Bioinformatics, 32(12):i121–i127, 2016

2016
[265]

An unchartered journey for ribosomes: circumnavigating circular rnas to produce proteins

Deirdre C Tatomer and Jeremy E Wilusz. An unchartered journey for ribosomes: circumnavigating circular rnas to produce proteins. Molecular cell, 66(1):1–2, 2017

2017
[266]

Chromatin accessibility prediction via convolu- tional long short-term memory networks with k-mer embedding

Xu Min, Wanwen Zeng, Ning Chen, Ting Chen, and Rui Jiang. Chromatin accessibility prediction via convolu- tional long short-term memory networks with k-mer embedding. Bioinformatics, 33(14):i92–i101, 2017

2017
[267]

Deephint: Understanding hiv-1 integration via deep learning with attention

Hailin Hu, An Xiao, Sai Zhang, Yangyang Li, Xuanling Shi, Tao Jiang, Linqi Zhang, Lei Zhang, and Jianyang Zeng. Deephint: Understanding hiv-1 integration via deep learning with attention. Bioinformatics, 35(10):1660– 1667, 2018

2018
[268]

Deep motif: Visualizing genomic sequence classiﬁcations

Jack Lanchantin, Ritambhara Singh, Zeming Lin, and Yanjun Qi. Deep motif: Visualizing genomic sequence classiﬁcations. arXiv preprint arXiv:1605.01133, 2016

2016 arXiv
[269]

Alternative splicing — Wikipedia, the free encyclopedia, 2019

Wikipedia contributors. Alternative splicing — Wikipedia, the free encyclopedia, 2019. [Online; accessed 6-August-2020]

2019
[270]

Reward sensitivity of acc as an intermediate phenotype between drd4-521t and substance misuse

Travis E Baker, Tim Stockwell, Gordon Barnes, Roderick Haesevoets, and Clay B Holroyd. Reward sensitivity of acc as an intermediate phenotype between drd4-521t and substance misuse. Journal of cognitive neuroscience, 28(3):460–471, 2016

2016
[271]

A deep learning approach for cancer detection and relevant gene identiﬁcation

Padideh Danaee, Reza Ghaeini, and David A Hendrix. A deep learning approach for cancer detection and relevant gene identiﬁcation. In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017, pages 219–229. World Scientiﬁc, 2017

2017
[272]

Deep genomic signature for early metastasis prediction in prostate cancer

Hossein Shariﬁ-Noghabi, Yang Liu, Nicholas Erho, Raunak Shrestha, Mohammed Alshalalfa, Elai Davicioni, Colin C Collins, and Martin Ester. Deep genomic signature for early metastasis prediction in prostate cancer. BioRxiv, page 276055, 2019

2019
[273]

Denoising genome-wide histone chip-seq with convolutional neural networks

Pang Wei Koh, Emma Pierson, and Anshul Kundaje. Denoising genome-wide histone chip-seq with convolutional neural networks. Bioinformatics, 33(14):i225–i233, 2017

2017
[274]

An unsupervised learning method for disease classiﬁcation based on dna methylation signatures

Mohammad Firouzi, Andrei Turinsky, Sanaa Choufani, Michelle T Siu, Rosanna Weksberg, and Michael Brudno. An unsupervised learning method for disease classiﬁcation based on dna methylation signatures. bioRxiv, page 492926, 2018. 39 A PREPRINT - AUGUST 11, 2020

2018
[275]

Massively multitask networks for drug discovery

Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, and Vijay Pande. Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072, 2015

2015 arXiv
[276]

Generating focused molecule libraries for drug discovery with recurrent neural networks

Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS central science, 4(1):120–131, 2017

2017
[277]

Chemical space mimicry for drug discovery

William Yuan, Dadi Jiang, Dhanya K Nambiar, Lydia P Liew, Michael P Hay, Joshua Bloomstein, Peter Lu, Brandon Turner, Quynh-Thu Le, Robert Tibshirani, et al. Chemical space mimicry for drug discovery. Journal of chemical information and modeling, 57(4):875–882, 2017

2017
[278]

Automatic chemical design using a data-driven continuous representation of molecules

Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez- Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru- Guzik. Automatic chemical design using a data-driven continuous re...

2018
[279]

drugan: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico

Artur Kadurin, Sergey Nikolenko, Kuzma Khrabrov, Alex Aliper, and Alex Zhavoronkov. drugan: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Molecular pharmaceutics, 14(9):3098–3104, 2017

2017
[280]

Application of generative autoencoder in de novo molecular design

Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, and Hongming Chen. Application of generative autoencoder in de novo molecular design. Molecular informatics, 37(1-2):1700123, 2018

2018
[281]

Deepproﬁle: Deep learning of cancer molecular proﬁles for precision medicine

Ayse Berceste Dincer, Saﬁye Celik, Naozumi Hiranuma, and Su-In Lee. Deepproﬁle: Deep learning of cancer molecular proﬁles for precision medicine. bioRxiv, page 278739, 2018

2018
[282]

Sequence tutor: Conservative ﬁne-tuning of sequence generation models with kl-control

Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E Turner, and Douglas Eck. Sequence tutor: Conservative ﬁne-tuning of sequence generation models with kl-control. In Proceedings of the 34th International Conference on Machine Learning-Volume...

2017
[283]

Machine learning and decision support in critical care

Alistair EW Johnson, Mohammad M Ghassemi, Shamim Nemati, Katherine E Niehaus, David A Clifton, and Gari D Clifford. Machine learning and decision support in critical care. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 104(2):444, 2016

2016
[284]

The power of the patient voice: learning indicators of treatment adherence from an online breast cancer forum

Zhijun Yin, Bradley Malin, Jeremy Warner, Pei-Yun Hsueh, and Ching-Hua Chen. The power of the patient voice: learning indicators of treatment adherence from an online breast cancer forum. In Eleventh International AAAI Conference on Web and Social Media, 2017

2017
[285]

Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients

Marta Amengual-Gual, Adriana Ulate-Campos, and Tobias Loddenkemper. Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients. Seizure, 2018

2018
[286]

Automated seizure detection systems and their effectiveness for each type of seizure

A Ulate-Campos, F Coughlin, M Gaínza-Lein, I Sánchez Fernández, PL Pearl, and T Loddenkemper. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure, 40:88–101, 2016

2016
[287]

Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact

Shaodian Zhang, Erin O’Carroll Bantum, Jason Owen, Suzanne Bakken, and Noémie Elhadad. Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact. Journal of the American Medical Informatics Association, 24(2):451–...

2017
[288]

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks

Shaodian Zhang, Edouard Grave, Elizabeth Sklar, and Noémie Elhadad. Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks. Journal of biomedical informatics, 69:1–9, 2017

2017
[289]

Cataloguing treatments discussed and used in online autism communities

Shaodian Zhang, Tian Kang, Lin Qiu, Weinan Zhang, Yong Yu, and Noémie Elhadad. Cataloguing treatments discussed and used in online autism communities. In Proceedings of the 26th International Conference on World Wide Web, pages 123–131. International World Wide Web Conferences...

2017
[290]

Knowledge sharing and social media: Altruism, perceived online attachment motivation, and perceived online relationship commitment

Will WK Ma and Albert Chan. Knowledge sharing and social media: Altruism, perceived online attachment motivation, and perceived online relationship commitment. Computers in Human Behavior, 39:51–58, 2014

2014
[291]

Social media usage: 2005-2015

Andrew Perrin. Social media usage: 2005-2015. Pew research center, pages 52–68, 2015

2005
[292]

Social media and loneliness: Why an instagram picture may be worth more than a thousand twitter words

Matthew Pittman and Brandon Reich. Social media and loneliness: Why an instagram picture may be worth more than a thousand twitter words. Computers in Human Behavior, 62:155–167, 2016

2016
[293]

The impact of social media on the sexual and social wellness of adolescents

Lisa M Cookingham and Ginny L Ryan. The impact of social media on the sexual and social wellness of adolescents. Journal of pediatric and adolescent gynecology, 28(1):2–5, 2015

2015
[294]

Monitoring depression trend on twitter during the covid-19 pandemic

Yipeng Zhang, Hanjia Lyu, Yubao Liu, Xiyang Zhang, Yu Wang, and Jiebo Luo. Monitoring depression trend on twitter during the covid-19 pandemic. arXiv preprint arXiv:2007.00228, 2020

2007 arXiv
[295]

Multidisciplinary research priorities for the covid-19 pandemic: a call for action for mental health science

Emily A Holmes, Rory C O’Connor, V Hugh Perry, Irene Tracey, Simon Wessely, Louise Arseneault, Clive Ballard, Helen Christensen, Roxane Cohen Silver, Ian Everall, et al. Multidisciplinary research priorities for the covid-19 pandemic: a call for action for mental health scienc...

2020
[296]

Wearable sensors for remote health monitoring

Sumit Majumder, Tapas Mondal, and M Deen. Wearable sensors for remote health monitoring. Sensors, 17(1):130, 2017

2017
[297]

Flexible and stretchable physical sensor integrated platforms for wearable human-activity monitoringand personal healthcare

Tran Quang Trung and Nae-Eung Lee. Flexible and stretchable physical sensor integrated platforms for wearable human-activity monitoringand personal healthcare. Advanced materials, 28(22):4338–4372, 2016

2016
[298]

Deep deterministic learning for pattern recognition of different cardiac diseases through the internet of medical things

Uzair Iqbal, Teh Wah, Muhammad Habib ur Rehman, Ghulam Mujtaba, Muhammad Imran, and Muhammad Shoaib. Deep deterministic learning for pattern recognition of different cardiac diseases through the internet of medical things. Journal of Medical Systems, 42, 12 2018

2018
[299]

Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models.Sensors, 19(11):2451, 2019

Mohsin Munir, Shoaib Ahmed Siddiqui, Muhammad Ali Chattha, Andreas Dengel, and Sheraz Ahmed. Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models.Sensors, 19(11):2451, 2019

2019
[300]

A deep learning approach for parkinson’s disease diagnosis from eeg signals

Shu Lih Oh, Yuki Hagiwara, U Raghavendra, Rajamanickam Yuvaraj, N Arunkumar, M Murugappan, and U Rajendra Acharya. A deep learning approach for parkinson’s disease diagnosis from eeg signals. Neural Computing and Applications, pages 1–7, 2018

2018

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [187]

Predicting need for vasopressors in the intensive care unit using an attention based deep learning model

Gloria Hyunjung Kwak, Lowell Ling, and Pan Hui. Predicting need for vasopressors in the intensive care unit using an attention based deep learning model. researchSquare preprint doi:10.21203/rs.3.rs-45876/v1, 2020

work page doi:10.21203/rs.3.rs-45876/v1 2020

[2] [64]

Gradient-based learning applied to document recognition

Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998

1998

[3] [73]

Long short-term memory

Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997

1997

[4] [57]

Support vector machine

V Vapnik. Support vector machine. Mach. Learn, 20:273–297, 1995

1995

[5] [62]

Efﬁcient estimation of word representations in vector space

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efﬁcient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013

arXiv 2013

[6] [1]

Deep learning for healthcare: review, opportunities and challenges

Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T Dudley. Deep learning for healthcare: review, opportunities and challenges. Brieﬁngs in bioinformatics, 19(6):1236–1246, 2017

2017

[7] [2]

Deep learning for health informatics

Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu-Perez, Benny Lo, and Guang- Zhong Yang. Deep learning for health informatics. IEEE journal of biomedical and health informatics , 21(1):4–21, 2016

2016

[8] [3]

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez. A survey on deep learning in medical image analysis. Medical image analysis, 42:60–88, 2017

2017