Pith. sign in

REVIEW 3 major objections 4 minor 180 references

MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read One shared graph-transformer model outperforms all comparison methods on average across four clinical tasks.

desk verdict The architecture is coherent and the post-hoc analysis is honest, but the central empirical claim is undermined by transductive leakage in graph construction and checkpoint selection on test AUROC. read the letter →

arxiv 2608.06430 v1 pith:XRHTIEHN submitted 2026-08-05 cs.LG q-bio.QM

Anirudh Rayas , Yuan Wang , Pavan Turaga This is my paper · ORCID
classification cs.LGq-bio.QM
keywords electronichealthrecordsheterogeneousgraphtransformermulti-tasklearningtemporalattentionnormalizedpointwisemutualinformationclinicalpredictionmortalityreadmission
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

MiGHT-EHR is a single model that tries to capture all three structures hidden in electronic health records at once: the web of clinical entities (patients, visits, diagnoses, procedures, prescriptions), the order of a patient's visits over time, and the statistical links between prediction tasks. The paper claims that a heterogeneous graph built with statistically grounded co-occurrence edges, combined with temporal attention and gradient-balanced multi-task training, learns one visit representation that serves four clinical tasks better on average than all comparison methods on two large critical-care databases. The headline result is an 8.71% relative average improvement over the reference multi-task baseline, driven mainly by mortality and readmission prediction, while drug recommendation and length-of-stay stay competitive. If true, a single shared model could support several clinical predictions at once while retaining interpretable structure in its representations.

What carries the argument

The load-bearing construction is the heterogeneous temporal EHR graph: five node types (patient, visit, diagnosis, procedure, prescription) joined by membership edges, directed next-visit edges between a patient's consecutive visits, and co-occurrence edges between concept nodes retained only when the normalized pointwise mutual information $\mathrm{NPMI}(a,b)=\frac{1}{-\log p(a,b)}\log\frac{p(a,b)}{p(a)p(b)}$ passes a threshold with a count floor. The encoder is a relation-aware graph transformer: two message-passing layers compute per-relation attention, per-node inter-relation attention, and gated residuals, followed by a causal cross-visit temporal attention in which each visit attends directly to all of its earlier visits; task losses are combined by DB-MTL, a gradient-balancing objective that equalizes the scale and magnitude of each task's update.

What would settle it

Rebuild the graph and pretrained features using only training-side visits, or only visits before a cutoff date, keeping everything else fixed, and compare test AUROC across the four tasks; if the average improvement over the reference multi-task baseline collapses, the pre-split graph construction is what carries the result.

Watch

Extended reading notes

Core claim

The paper's central claim is that representing electronic health records as a heterogeneous temporal graph, rather than as a sequence or a plain heterogeneous graph, lets four clinical tasks be learned from one shared encoder. Each visit is a node whose features come from frozen clinical text embeddings for concept nodes and structure-based embeddings for patients and visits; edges are of three kinds: membership links tying each visit to its patient and its diagnoses, procedures, and prescriptions; directed next-visit edges that keep the encoding causal; and co-occurrence edges between concepts retained only when normalized pointwise mutual information passes a threshold. A two-layer relation-aware message-passing transformer followed by cross-visit temporal attention produces one representation per visit, and dual-balancing gradient equalization prevents any single task from dominating the shared parameters. On the two evaluation databases the model is the only compared method with a positive average relative improvement over the reference multi-task baseline ($\Delta_p = +8.71\%$), with the gains concentrated in mortality ($+22.16\%$) and readmission ($+17.90\%$); drug recommendation and length of stay remain competitive. Post-hoc probing shows patient neighborhoods become organized by outcome rather than by diagnosis, clinical concepts appear as linear directions in the representation space, and calibration is good for the drug and length-of-stay tasks.

Load-bearing premise

The graph statistics and the pretrained node features are computed from all records, test visits included, before the 90/10 split, so the reported test gains rest on the assumption that those test visits do not leak label-relevant information into the representation.

Editorial extensions

If this is right

  • A single shared encoder can serve four heterogeneous tasks and is the only compared method with a positive average relative improvement over the reference multi-task baseline, so multi-task sharing does not have to come at the price of single-task accuracy.
  • The rare-event tasks of mortality and readmission are where the model gains most (+22.16% and +17.90% relative), while drug recommendation and length of stay remain within about one point of specialized methods.
  • Because the NPMI criterion discards 75–90% of observed concept pairs, the reported results imply that association-based edges, not raw co-occurrence counts, are what make the graph informative.
  • The post-hoc analyses show patient neighborhoods increasingly share clinical outcomes with depth while diagnostic and comorbidity controls stay flat, implying the representation separates prognosis from diagnosis.
  • Calibration is task-dependent: drug and length-of-stay probabilities are nearly calibrated on both databases, while mortality is mildly overconfident on the smaller database and strongly overconfident on the larger one, so those probabilities should be recalibrated before clinical use.

Reading between the lines

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

  • Rebuilding the graph and pretrained features only from admissions before the prediction window would test whether the method's advantage survives real deployment, where future visits are not available when the model is built.
  • Because the three relation categories are not hospital-specific, the same recipe of association-thresholded edges plus causal temporal attention could transfer to insurance claims or outpatient registries with different concept vocabularies and visit structures.
  • An ablation that replaces the gradient-balancing objective with plain loss weighting would isolate how much of the rare-event gain comes from optimization balance rather than from graph structure or temporal attention.
  • The paper's pairwise co-occurrence edges only approximate multi-drug interactions; modeling the set of concepts in a visit as a hyperedge is a concrete next step that would likely help the drug-recommendation task most.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper proposes MiGHT-EHR, a heterogeneous graph transformer for multi-task prediction from temporal EHRs. It constructs a five-node-type graph with membership, temporal, and NPMI-based co-occurrence edges; initializes concept nodes with Bio_ClinicalBERT text embeddings and patient/visit nodes with TransE embeddings; and trains a shared two-layer relation-aware message-passing encoder with cross-visit temporal attention and DB-MTL gradient balancing. On MIMIC-III and MIMIC-IV it reports the best average relative improvement over MulT-EHR across mortality, readmission, length-of-stay, and drug recommendation, and presents post-hoc analyses of neighborhood purity, linear concept probes, and calibration. The central claim is empirical: a single shared model achieves the highest average performance across four tasks among fourteen methods.

Significance. If the empirical claim were established under a clean inductive protocol, the paper would be a useful contribution: it jointly models heterogeneity, temporality, and task sharing in one architecture; it uses a statistically motivated edge criterion (NPMI) rather than raw co-occurrence counts; and it reports graph statistics, hyperparameters, and an honest limitation on MIMIC-IV mortality calibration. The post-hoc analyses include sensible control conditions, such as comparing outcome-neighborhood purity with comorbidity-burden purity. However, the current evaluation does not establish the central claim because the graph and pretrained features are built on the full dataset including test visits, and because the checkpoint is selected on test AUROC. These are protocol-level issues that affect every reported comparison and must be fixed before the performance claims can be assessed.

major comments (3)
  1. [§4.1, §4.2, §5.1] The graph construction and node-feature pretraining are not split-aware. In §4.1, NPMI in Eq. (3) is computed with N = |V_vis| over all visits, so co-occurrence edges are selected using test-visit co-occurrence statistics; in §4.2 and Appendix E, TransE is pretrained on the full graph with membership and next_visit relations incident to test visits. Because message passing in §4.3 operates on this graph and the pretrained features are frozen into test-visit representations, the reported test AUROC/AUPR values for MiGHT-EHR are produced under a transductive protocol, whereas the baselines in Tables 1–2 are trained on the 90% split without access to test visits or test-derived statistics. The claim in §5.4 and Table 3 that MiGHT-EHR is the only method with positive average improvement therefore does not follow from an apples-to-apples comparison. The experiments need to be re-run with graph construction and TransE pretraining restricted to the training split, or all baselines must be given the same transductive access as MiGHT-EHR.
  2. [§5.1, Appendix E] Model selection is performed on the test set: the reported results come from "the checkpoint maximizing the mean test AUROC across the four tasks." Selecting a checkpoint over 1000 training epochs using the test labels invalidates the test AUROC/AUPR as an unbiased estimate and can systematically inflate the reported numbers, especially for a high-capacity model. A validation split should be used for checkpoint selection, and the test set should be reserved for a single final evaluation. This issue affects the validity of the headline comparison even if the same rule were applied to all methods.
  3. [Table 3, §5.4] The average improvement claim is based on a single run with one seed (seed 612, Appendix E), and no confidence intervals or significance tests are reported. Since the headline result is a single aggregate number and the gains are concentrated in two rare-event tasks, the absence of uncertainty quantification makes it impossible to tell whether the reported +8.71% is robust or within run-to-run variation. Multiple seeds with error bars, or a paired significance test, are needed to support the central empirical claim.
minor comments (4)
  1. [Table 1] In the MiGHT-EHR row, the MIMIC-III length-of-stay accuracy is reported as 642.22, which is impossible for a percentage; this appears to be a typo for 64.22.
  2. [§5.4 vs Table 3] The text in §5.4 states Δp = +8.61%, while Table 3 and Eq. (15) give +8.71%; these numbers should be made consistent.
  3. [§5.3, Tables 1–2] GraphCare (Jiang et al. 2023) is listed as a comparable method in §5.3 but its results do not appear in Tables 1 or 2; either its results should be included or it should be removed from the comparison list.
  4. [Appendix F, Table 12] The appendix candidly reports high raw ECE for MIMIC-IV mortality (0.461) and readmission (0.241); this disclosure is appreciated, but the main text's claim that predicted probabilities are "well calibrated" should be qualified to note that this holds only for drug recommendation and length-of-stay on MIMIC-IV.

Circularity Check

1 steps flagged · score 6.0 of 10

No theoretical circularity, but the headline +8.71% result is selected by maximizing test AUROC, so the central empirical claim partially reduces to the evaluation criterion.

  1. fitted input called prediction [Appendix E (Checkpoint selection; also Section 5.1)]
    "The reported checkpoint is the epoch maximizing the mean of the four test AUROCs; every task is read from that same checkpoint."

    By the paper's own protocol, the test AUROC reported for every model is not an independent estimate: it is the value that was maximized over epochs using the test labels. Tables 1-3 and the average improvement Delta_p are therefore computed from in-sample maxima of the test criterion rather than from genuine out-of-sample predictions. The claim that MiGHT-EHR is the best multi-task predictor (Delta_p = +8.71%) is thus partly determined by construction: the reported number is the selected optimum of the same metric used to evaluate it. This is a fitted model-selection input renamed as a prediction.

full rationale

The method itself is an empirical pipeline rather than a derivation: NPMI edges are an unsupervised co-occurrence statistic, TransE pretraining optimizes a link-prediction objective on membership and temporal relations, and DB-MTL only reweights task gradients; none of these components is defined in terms of the task labels or the reported metrics, so there is no self-definitional or self-citation-based circularity. The one genuine selection-by-construction step is the checkpoint rule: the epoch that maximizes mean test AUROC is chosen, and the same test AUROC is then reported, so the headline numbers are in-sample maxima of the evaluation criterion rather than out-of-sample predictions, which directly affects the central empirical claim and supports a score of 6. Separately, the graph construction and TransE pretraining use all visits including the test split before the 90/10 split; this is a transductive-leakage soundness concern that could inflate MiGHT-EHR relative to inductive baselines, but it is not circularity because test labels are not used and no equation equates the output to the input. The paper also honestly flags high mortality ECE on MIMIC-IV as a limitation, which is a correctness statement rather than a circular step.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claim depends on the choice of NPMI threshold and count floor for graph edges, on the use of frozen pretrained features, and on the multi-task balancing scheme. The most serious hidden premise is that building the graph and pretraining on the full dataset does not leak test information into the training representation.

free parameters (7)
  • NPMI threshold tau = 0.10
    Chosen by hand (Sec 4.1, Appendix E) to decide which co-occurrence edges are kept; directly controls graph connectivity.
  • Co-occurrence count floor kappa = 5
    Chosen by hand (Sec 4.1, Appendix E) as the minimum number of shared visits for an edge; discards rare pairs.
  • Node/hidden embedding dimension d_in = d = 128
    Chosen by hand (Sec 4.2, Appendix E); sets the size of all representations.
  • Number of message-passing layers L = 2
    Architecture choice (Sec 4.3, Appendix E) that controls the receptive field.
  • Attention heads H = 8
    Architecture choice (Appendix E).
  • DB-MTL momentum rho = 0.9
    Hyperparameter of the gradient balancing rule (Sec 4.3, Appendix E).
  • Drug logit temperature floor = 0.1
    Annealed temperature floor for drug recommendation logits (Appendix E), chosen to keep the loss informative.
assumptions (5)
  • domain assumption Co-occurrence beyond chance (NPMI above tau) marks clinically meaningful associations.
    Sec 4.1 retains edges only when NPMI >= 0.10 and co-occurrence count >= 5; this assumes that NPMI captures real clinical relationships useful for prediction.
  • domain assumption Frozen text and structural embeddings provide a good starting point for the downstream tasks.
    Sec 4.2 initializes concept nodes with Bio_ClinicalBERT and patient/visit nodes with TransE, and holds them fixed; this assumes these pretrained features are informative.
  • domain assumption Gradient balancing improves multi-task learning over simple loss weighting.
    Sec 4.3 uses DB-MTL (Lin et al. 2023) to equalize gradient scales and magnitudes; the paper relies on this method improving shared representations.
  • domain assumption Using the full dataset to construct the graph and pretrain features does not leak test information.
    Sec 4.1 and Sec 4.2 operate on all visits before the 90/10 split; the validity of the evaluation depends on this assumption, which is not defended in the paper.
  • domain assumption A visit-level 90/10 split is an appropriate train/test partition for evaluating the model.
    Sec 5.1 uses a visit-level split; this assumes visits are the unit of evaluation and that the split is not contaminated by the graph construction.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records." pith.science (2026). https://pith.science/paper/XRHTIEHN

@misc{pith2026260806430,
  author       = {Pith},
  title        = {Pith review of: MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XRHTIEHN}},
  note         = {Machine review of arXiv:2608.06430}
}
read the original abstract

Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction. However, effective learning remains challenging because EHRs encode heterogeneous, temporally ordered clinical interactions. In particular, EHRs contain: (i) heterogeneous clinical entities, including patients, visits, diagnoses, prescriptions, and procedures, together with their heterogeneous interactions, (ii) longitudinal patient trajectories across hospital visits and (iii) shared statistical dependencies across related clinical prediction tasks. Existing EHR learning methods capture only a subset of these properties. To bridge this gap, we propose Multi-task Graph transformer for Heterogeneous Temporal EHRs (MiGHT-EHR), which jointly models all three within a unified representation learning method. MiGHT-EHR constructs a heterogeneous graph from EHRs in which nodes represent clinical entities and edges connect statistically associated entities identified via normalized point-wise mutual information. Across MIMIC-III and MIMIC-IV datasets, MiGHT-EHR outperforms state-of-the-art methods on average across four tasks: drug recommendation, prediction of length-of-stay, mortality, and readmission, with particularly strong improvements in mortality and readmission prediction. Furthermore, a post-hoc analysis of the learned representations reveals that patient neighborhoods are organized by clinical outcomes, salient medical concepts are recoverable as linear directions in the representation space, and task probabilities are well calibrated. Collectively, these findings demonstrate that MiGHT-EHR representations support diverse prediction tasks while preserving clinically interpretable structure.

Figures

Figures reproduced from arXiv: 2608.06430 by the authors.

Figure 1
Figure 1. Overview of MiGHT-EHR. Stage 1: patient records become a graph of five node types, linked by membership edges (edges linking each visit to its patient and its concept nodes), temporal edges (directed edges between a patient’s consecutive visits), and co-occurrence edges (edges between co-occurring concept nodes determined by NPMI values). Stage 2: node features are pretrained and frozen: text embeddings for concepts… view at source ↗
Figure 2
Figure 2. Post-hoc analysis on frozen MIMIC-III. Left: [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

180 extracted references · 42 canonical work pages

  1. [1]

    AI for Critical Infrastructure Workshop@ IJCAI-24 , year=

    GraphEHR: Heterogeneous graph neural network for electronic health records , author=. AI for Critical Infrastructure Workshop@ IJCAI-24 , year=

  2. [2]

    Proceedings of the conference on health, inference, and learning , pages=

    Variationally regularized graph-based representation learning for electronic health records , author=. Proceedings of the conference on health, inference, and learning , pages=

  3. [3]

    Neural Networks , volume =

    Multi-task heterogeneous graph learning on electronic health records , author =. Neural Networks , volume =. 2024 , issn =. doi:10.1016/j.neunet.2024.106644 , publisher =

    arXiv 2024

  4. [4]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Learning the graphical structure of electronic health records with graph convolutional transformer , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  5. [5]

    Scientific Reports , year=

    Heterogeneous graph neural network-based prediction of immune-related adverse events , author=. Scientific Reports , year=

  6. [6]

    IEEE Journal of Biomedical and Health Informatics , volume=

    Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks , author=. IEEE Journal of Biomedical and Health Informatics , volume=. 2023 , publisher=

    2023

  7. [7]

    International Conference on Artificial Intelligence in Medicine , pages=

    Heterogeneous graph embeddings of electronic health records improve critical care disease predictions , author=. International Conference on Artificial Intelligence in Medicine , pages=. 2020 , organization=

    2020

  8. [8]

    Hoyt, Garrik and Chatterjee, Noyonica and Battaglia, Fortunato and Basu, Paramita , month = feb, year =. Medical. doi:10.48550/arXiv.2502.09781 , urldate =

    work page Pith review arXiv doi:10.48550/arxiv.2502.09781

Show all 180 references
  1. [9]

    doi:10.48550/arXiv.2511.01249 , urldate =

    Lin, Kun-Wei and Kuo, Yu-Chen and Wang, Hsin-Yao and Tseng, Yi-Ju , month = nov, year =. doi:10.48550/arXiv.2511.01249 , urldate =

    doi

  2. [10]

    NeurIPS 2025 Workshop on Learning from Time Series for Health , year =

    Predicting Dementia Risk Using Longitudinal Electronic Health Records Data , author=. NeurIPS 2025 Workshop on Learning from Time Series for Health , year =

    2025

  3. [11]

    bioRxiv , pages=

    DyGraphTrans: A temporal graph representation learning framework for modeling disease progression from Electronic Health Records , author=. bioRxiv , pages=. 2026 , publisher=

    2026

  4. [12]

    Temporal Graph Learning Workshop@ KDD 2025 , year=

    Multi-Modal Interpretable Graph for Competing Risk Prediction with Electronic Health Records , author=. Temporal Graph Learning Workshop@ KDD 2025 , year=

    2025

  5. [13]

    IJCAI: proceedings of the conference , volume=

    Predictive modeling with temporal graphical representation on electronic health records , author=. IJCAI: proceedings of the conference , volume=

  6. [14]

    International Journal of Medical Informatics , pages=

    Graph attention network with comorbidity connectivity embedding for post-traumatic epilepsy risk prediction using sparse time-series electronic health records , author=. International Journal of Medical Informatics , pages=. 2025 , publisher=

    2025

  7. [15]

    Journal of biomedical informatics , volume=

    Harmonized representation learning on dynamic EHR graphs , author=. Journal of biomedical informatics , volume=. 2020 , publisher=

    2020

  8. [16]

    Journal of Discrete Mathematical Sciences and Cryptography , volume=

    Construction of heterogeneous medical knowledge graph from electronic health records , author=. Journal of Discrete Mathematical Sciences and Cryptography , volume=. 2022 , publisher=

    2022

  9. [17]

    IEEE Journal of Biomedical and Health Informatics , volume=

    Disease prediction via graph neural networks , author=. IEEE Journal of Biomedical and Health Informatics , volume=. 2020 , publisher=

    2020

  10. [18]

    NPJ digital medicine , volume=

    Synthetic electronic health records generated with variational graph autoencoders , author=. NPJ digital medicine , volume=. 2023 , publisher=

    2023

  11. [19]

    2025 , school=

    Temporal graph-based convolutional neural networks for electronic health records , author=. 2025 , school=

    2025

  12. [20]

    IEEE Transactions on Big Data , year=

    An Empirical Study Challenging the Ability of Heterogeneous Graph Neural Networks to Effectively Learn Topological Structures , author=. IEEE Transactions on Big Data , year=

  13. [21]

    Journal of Biomedical Informatics , volume=

    Graph neural networks for clinical risk prediction based on electronic health records: A survey , author=. Journal of Biomedical Informatics , volume=. 2024 , publisher=

    2024

  14. [22]

    Information Processing & Management , volume=

    FairCare: Adversarial training of a heterogeneous graph neural network with attention mechanism to learn fair representations of electronic health records , author=. Information Processing & Management , volume=. 2024 , publisher=

    2024

  15. [23]

    Knowledge-

    Mulyadi, Ahmad Wisnu and Suk, Heung-Il , month = jun, year =. Knowledge-. doi:10.48550/arXiv.2310.14552 , urldate =

    doi arXiv

  16. [24]

    Vijay and El-Yaagoubi, Anass B

    Anand, D. Vijay and El-Yaagoubi, Anass B. and Ombao, Hernando and Chung, Moo K. , month = jul, year =. Hodge-. doi:10.48550/arXiv.2211.10542 , urldate =

    doi arXiv

  17. [25]

    IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

    HL-HGAT: Heterogeneous Graph Attention Network via Hodge-Laplacian Operator , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

  18. [26]

    Heterogeneous

    Liu, Huan and Jiao, Pengfei and Gao, Mengzhou and Chen, Chaochao and Jin, Di , month = jun, year =. Heterogeneous. doi:10.48550/arXiv.2506.17312 , urldate =

    doi arXiv

  19. [27]

    Heterogeneous

    Sun, Xiangguo and Yin, Hongzhi and Liu, Bo and Chen, Hongxu and Cao, Jiuxin and Shao, Yingxia and Hung, Nguyen Quoc Viet , month = jan, year =. Heterogeneous. doi:10.48550/arXiv.2010.10728 , urldate =

    2010 doi arXiv

  20. [28]

    International Semantic Web Conference , pages=

    HypKG: Hypergraph-Based Knowledge Graph Contextualization for Precision Healthcare , author=. International Semantic Web Conference , pages=. 2025 , organization=

    2025

  21. [29]

    Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining , pages=

    Self-supervised time-aware heterogeneous hypergraph learning for dynamic graph-level classification , author=. Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining , pages=

  22. [30]

    doi:10.48550/arXiv.2603.18459 , urldate =

    Zhang, Xiangxu and Zhou, Xiao and Xu, Hongteng and Lian, Jianxun , month = mar, year =. doi:10.48550/arXiv.2603.18459 , urldate =

    doi

  23. [31]

    Heterogeneous

    Hu, Ziniu and Dong, Yuxiao and Wang, Kuansan and Sun, Yizhou , month = mar, year =. Heterogeneous. doi:10.48550/arXiv.2003.01332 , urldate =

    2003 doi arXiv

  24. [32]

    AMIA Summits on Translational Science Proceedings , volume=

    Hypergraph transformers for ehr-based clinical predictions , author=. AMIA Summits on Translational Science Proceedings , volume=

  25. [33]

    Advances in neural information processing systems , volume=

    Hypergcn: A new method for training graph convolutional networks on hypergraphs , author=. Advances in neural information processing systems , volume=

  26. [34]

    Advances in Neural Information Processing Systems , volume=

    Defining and Discovering Hyper-meta-paths for Heterogeneous Hypergraphs , author=. Advances in Neural Information Processing Systems , volume=

  27. [35]

    IEEE Access , year=

    Uncovering Clinical Insights From Electronic Health Records Through a Graph Neural Network for Diabetes Prediction , author=. IEEE Access , year=

  28. [36]

    Predicting

    Rocheteau, Emma and Tong, Catherine and Veličković, Petar and Lane, Nicholas and Liò, Pietro , month = jan, year =. Predicting. doi:10.48550/arXiv.2101.03940 , urldate =

    doi arXiv

  29. [37]

    Kim, Sunwoo and Lee, Soo Yong and Gao, Yue and Antelmi, Alessia and Polato, Mirko and Shin, Kijung , month = aug, year =. A. Proceedings of the 30th. doi:10.1145/3637528.3671457 , urldate =

  30. [38]

    Abubaker, Abdalgader and Maehara, Takanori and Nimishakavi, Madhav and Plachouras, Vassilis , month = nov, year =. Self-. doi:10.48550/arXiv.2311.11368 , urldate =

    doi arXiv

  31. [39]

    and Kwok, James T

    Lin, Baijiong and Jiang, Weisen and Ye, Feiyang and Zhang, Yu and Chen, Pengguang and Chen, Ying-Cong and Liu, Shu and Tsang, Ivor W. and Kwok, James T. , month = nov, year =. Dual-. doi:10.48550/arXiv.2308.12029 , urldate =

    doi

  32. [40]

    arXiv preprint arXiv:1710.10903 , year=

    Graph attention networks , author=. arXiv preprint arXiv:1710.10903 , year=

    arXiv

  33. [41]

    International Conference on Learning Representations , year=

    How Powerful are Graph Neural Networks? , author=. International Conference on Learning Representations , year=

  34. [42]

    Advances in neural information processing systems , volume=

    Graph transformer networks , author=. Advances in neural information processing systems , volume=

  35. [43]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Exploiting edge features for graph neural networks , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  36. [44]

    2022 IEEE International Conference on Knowledge Graph (ICKG) , pages=

    SUMSHINE: Scalable Unsupervised Multi-Source Heterogeneous Information Network Embeddings , author=. 2022 IEEE International Conference on Knowledge Graph (ICKG) , pages=. 2022 , organization=

    2022

  37. [45]

    Data Intelligence , pages=

    Source-Aware Embedding Training on Heterogeneous Information Networks , author=. Data Intelligence , pages=

  38. [46]

    Semi-supervised classification with graph convolutional networks , author=. J. International Conference on Learning Representations (ICLR 2017) , year=

    2017

  39. [47]

    Proceedings of the 2018 World Wide Web Conference , pages=

    Dual graph convolutional networks for graph-based semi-supervised classification , author=. Proceedings of the 2018 World Wide Web Conference , pages=

    2018

  40. [48]

    IEEE transactions on neural networks , volume=

    The graph neural network model , author=. IEEE transactions on neural networks , volume=. 2008 , publisher=

    2008

  41. [49]

    Proceedings

    A new model for learning in graph domains , author=. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. , volume=. 2005 , organization=

    2005

  42. [50]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Asap: Adaptive structure aware pooling for learning hierarchical graph representations , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  43. [51]

    Advances in neural information processing systems , volume=

    Diffgcn: Graph convolutional networks via differential operators and algebraic multigrid pooling , author=. Advances in neural information processing systems , volume=

  44. [52]

    arXiv preprint arXiv:1511.05493 , year=

    Gated graph sequence neural networks , author=. arXiv preprint arXiv:1511.05493 , year=

    arXiv

  45. [53]

    arXiv preprint arXiv:2004.11198 , year=

    Sign: Scalable inception graph neural networks , author=. arXiv preprint arXiv:2004.11198 , year=

    2004 arXiv

  46. [54]

    arXiv preprint arXiv:2104.09376 , year=

    Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training , author=. arXiv preprint arXiv:2104.09376 , year=

    arXiv

  47. [55]

    AI Open , volume=

    Graph neural networks: A review of methods and applications , author=. AI Open , volume=. 2020 , publisher=

    2020

  48. [56]

    arXiv preprint arXiv:2011.14867 , year=

    A survey on heterogeneous graph embedding: methods, techniques, applications and sources , author=. arXiv preprint arXiv:2011.14867 , year=

    2011 arXiv

  49. [57]

    IEEE Transactions on Neural Networks and Learning Systems , year=

    A survey on knowledge graphs: Representation, acquisition, and applications , author=. IEEE Transactions on Neural Networks and Learning Systems , year=

  50. [58]

    Hamilton and Rex Ying and Jure Leskovec , title =

    William L. Hamilton and Rex Ying and Jure Leskovec , title =. CoRR , volume =. 2017 , url =

    2017

  51. [59]

    GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs , journal =

    Jiani Zhang and Xingjian Shi and Junyuan Xie and Hao Ma and Irwin King and Dit. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs , journal =. 2018 , url =

    2018

  52. [60]

    arXiv preprint arXiv:2012.14722 , year=

    Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning , author=. arXiv preprint arXiv:2012.14722 , year=

    2012 arXiv

  53. [61]

    The World Wide Web Conference , pages=

    Heterogeneous graph attention network , author=. The World Wide Web Conference , pages=

  54. [62]

    Proceedings of The Web Conference 2020 , pages=

    Heterogeneous graph transformer , author=. Proceedings of The Web Conference 2020 , pages=

    2020

  55. [63]

    European semantic web conference , pages=

    Modeling relational data with graph convolutional networks , author=. European semantic web conference , pages=. 2018 , organization=

    2018

  56. [64]

    Advances in neural information processing systems , volume=

    Attention is all you need , author=. Advances in neural information processing systems , volume=

  57. [65]

    Advances in neural information processing systems , volume=

    Translating embeddings for modeling multi-relational data , author=. Advances in neural information processing systems , volume=

  58. [66]

    Knowledge graph embedding via dynamic mapping matrix , author=. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , pages=

  59. [67]

    Multi-relational poincar

    Balazevic, Ivana and Allen, Carl and Hospedales, Timothy , journal=. Multi-relational poincar

  60. [68]

    International conference on machine learning , pages=

    Complex embeddings for simple link prediction , author=. International conference on machine learning , pages=. 2016 , organization=

    2016

  61. [69]

    Proceedings of the IEEE , volume=

    A review of relational machine learning for knowledge graphs , author=. Proceedings of the IEEE , volume=. 2015 , publisher=

    2015

  62. [70]

    arXiv preprint arXiv:1412.6575 , year=

    Embedding entities and relations for learning and inference in knowledge bases , author=. arXiv preprint arXiv:1412.6575 , year=

    arXiv

  63. [71]

    Twenty-ninth AAAI conference on artificial intelligence , year=

    Learning entity and relation embeddings for knowledge graph completion , author=. Twenty-ninth AAAI conference on artificial intelligence , year=

  64. [72]

    HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction , year=

    Yang, Kuo and Wang, Ruyu and Liu, Guangming and Shu, Zixin and Wang, Ning and Zhang, Runshun and Yu, Jian and Chen, Jianxin and Li, Xiaodong and Zhou, Xuezhong , journal=. HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction , year=

  65. [73]

    Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks , author=

  66. [74]

    arXiv preprint arXiv:2005.00687 , year=

    Open graph benchmark: Datasets for machine learning on graphs , author=. arXiv preprint arXiv:2005.00687 , year=

    2005 arXiv

  67. [75]

    arXiv preprint arXiv:2007.08663 , year=

    Tudataset: A collection of benchmark datasets for learning with graphs , author=. arXiv preprint arXiv:2007.08663 , year=

    2007 arXiv

  68. [76]

    International conference on machine learning , pages=

    Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations , author=. International conference on machine learning , pages=. 2019 , organization=

    2019

  69. [77]

    , author=

    Domain Adaptive Classification on Heterogeneous Information Networks. , author=. IJCAI , pages=

  70. [78]

    ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) , volume=

    Distribution Aligned Multimodal and Multi-Domain Image Stylization , author=. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) , volume=. 2021 , publisher=

    2021

  71. [79]

    arXiv preprint arXiv:2012.05688 , year=

    DA-HGT: Domain Adaptive Heterogeneous Graph Transformer , author=. arXiv preprint arXiv:2012.05688 , year=

    2012 arXiv

  72. [80]

    arXiv preprint arXiv:1906.00684 , year=

    Dane: Domain adaptive network embedding , author=. arXiv preprint arXiv:1906.00684 , year=

    1906 arXiv

  73. [81]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    Adversarial discriminative domain adaptation , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  74. [82]

    Proceedings of the European Conference on Computer Vision (ECCV) , pages=

    Graph adaptive knowledge transfer for unsupervised domain adaptation , author=. Proceedings of the European Conference on Computer Vision (ECCV) , pages=

  75. [83]

    Advances in neural information processing systems , volume=

    Analysis of representations for domain adaptation , author=. Advances in neural information processing systems , volume=. 2007 , publisher=

    2007

  76. [84]

    Communications of the ACM , volume=

    Generative adversarial networks , author=. Communications of the ACM , volume=. 2020 , publisher=

    2020

  77. [85]

    Proceedings of the VLDB Endowment , volume=

    Pathsim: Meta path-based top-k similarity search in heterogeneous information networks , author=. Proceedings of the VLDB Endowment , volume=. 2011 , publisher=

    2011

  78. [86]

    IEEE Transactions on Knowledge and Data Engineering , volume=

    Heterogeneous information network embedding for recommendation , author=. IEEE Transactions on Knowledge and Data Engineering , volume=. 2018 , publisher=

    2018

  79. [87]

    arXiv preprint arXiv:1812.06229 , year=

    Domain-to-domain translation model for recommender system , author=. arXiv preprint arXiv:1812.06229 , year=

    arXiv

  80. [88]

    Journal of Cheminformatics , volume=

    kGCN: a graph-based deep learning framework for chemical structures , author=. Journal of Cheminformatics , volume=. 2020 , publisher=

    2020

  81. [89]

    Conference on learning theory , pages=

    A theoretical analysis of NDCG type ranking measures , author=. Conference on learning theory , pages=. 2013 , organization=

    2013

  82. [90]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  83. [91]

    arXiv preprint arXiv:1505.03906 , year=

    Training generative neural networks via maximum mean discrepancy optimization , author=. arXiv preprint arXiv:1505.03906 , year=

    arXiv

  84. [92]

    International Symposium onInformation Theory, 2004

    Jensen-Shannon divergence and Hilbert space embedding , author=. International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings. , pages=. 2004 , organization=

    2004

  85. [93]

    The International Conference on Learning Representations (ICLR 2022) , year=

    Revisiting Over-smoothing in BERT from the Perspective of Graph , author=. The International Conference on Learning Representations (ICLR 2022) , year=

    2022

  86. [94]

    International Conference on Learning Representations , year=

    PairNorm: Tackling Oversmoothing in GNNs , author=. International Conference on Learning Representations , year=

  87. [95]

    International Conference on Learning Representations , year=

    Understanding Over-parameterization in Generative Adversarial Networks , author=. International Conference on Learning Representations , year=

  88. [96]

    Cell reports , volume=

    Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images , author=. Cell reports , volume=. 2018 , publisher=

    2018

  89. [97]

    A Threshold Selection Method from Gray-Level Histograms , year=

    Otsu, Nobuyuki , journal=. A Threshold Selection Method from Gray-Level Histograms , year=

  90. [98]

    International Conference on Machine Learning , pages=

    Efficientnet: Rethinking model scaling for convolutional neural networks , author=. International Conference on Machine Learning , pages=. 2019 , organization=

    2019

  91. [99]

    International Conference on Machine Learning , pages=

    Efficientnetv2: Smaller models and faster training , author=. International Conference on Machine Learning , pages=. 2021 , organization=

    2021

  92. [100]

    Medical image analysis , volume=

    RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification , author=. Medical image analysis , volume=. 2019 , publisher=

    2019

  93. [101]

    International Conference on Medical Image Computing and Computer-Assisted Intervention , pages=

    Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks , author=. International Conference on Medical Image Computing and Computer-Assisted Intervention , pages=. 2021 , organization=

    2021

  94. [102]

    Thirty-sixth AAAI conference on artificial intelligence , year=

    H2-MIL: Exploring Hierarchical Representation with Heterogeneous Multiple Instance Learning for Whole Slide Image Analysis , author=. Thirty-sixth AAAI conference on artificial intelligence , year=

  95. [103]

    Medical Image Analysis , volume=

    Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images , author=. Medical Image Analysis , volume=. 2019 , publisher=

    2019

  96. [104]

    2020 , school=

    Kimianet: Training a deep network for histopathology using high-cellularity , author=. 2020 , school=

    2020

  97. [105]

    Computerized Medical Imaging and Graphics , pages=

    A survey on graph-based deep learning for computational histopathology , author=. Computerized Medical Imaging and Graphics , pages=. 2021 , publisher=

    2021

  98. [106]

    Medical image analysis , volume=

    Hierarchical graph representations in digital pathology , author=. Medical image analysis , volume=. 2022 , publisher=

    2022

  99. [107]

    Cancers , volume=

    Multi-Stage Classification-Based Deep Learning for Gleason System Grading Using Histopathological Images , author=. Cancers , volume=. 2022 , publisher=

    2022

  100. [108]

    arXiv preprint arXiv:2111.04740 , year=

    BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images , author=. arXiv preprint arXiv:2111.04740 , year=

    arXiv

  101. [109]

    2017 , publisher=

    Elements of causal inference: foundations and learning algorithms , author=. 2017 , publisher=

    2017

  102. [110]

    and Winship, Christopher , year=

    Morgan, Stephen L. and Winship, Christopher , year=. Counterfactuals and Causal Inference: Methods and Principles for Social Research , DOI=

  103. [111]

    Frontiers in genetics , volume=

    Review of causal discovery methods based on graphical models , author=. Frontiers in genetics , volume=. 2019 , publisher=

    2019

  104. [112]

    , author=

    Learning Bayesian networks with discrete variables from data. , author=. KDD , volume=

  105. [113]

    arXiv preprint arXiv:2101.10373 , year=

    Bayesian Pyramids: Identifiable Multilayer Discrete Latent Structure Models for Discrete Data , author=. arXiv preprint arXiv:2101.10373 , year=

    arXiv

  106. [114]

    Causal Screening to Interpret Graph Neural Networks , author=

  107. [115]

    International Conference on Machine Learning , pages=

    Neural network attributions: A causal perspective , author=. International Conference on Machine Learning , pages=. 2019 , organization=

    2019

  108. [116]

    Econometrica: journal of the Econometric Society , pages=

    Investigating causal relations by econometric models and cross-spectral methods , author=. Econometrica: journal of the Econometric Society , pages=. 1969 , publisher=

    1969

  109. [117]

    The Annals of Applied Statistics , volume=

    BART: Bayesian additive regression trees , author=. The Annals of Applied Statistics , volume=. 2010 , publisher=

    2010

  110. [118]

    Advances in neural information processing systems , volume=

    Nonlinear causal discovery with additive noise models , author=. Advances in neural information processing systems , volume=

  111. [119]

    International Conference on Machine Learning , pages=

    DAG-GNN: DAG structure learning with graph neural networks , author=. International Conference on Machine Learning , pages=. 2019 , organization=

    2019

  112. [120]

    International Conference on Machine Learning , pages=

    Causal transformer for estimating counterfactual outcomes , author=. International Conference on Machine Learning , pages=. 2022 , organization=

    2022

  113. [121]

    BaCaDI: Bayesian Causal Discovery with Unknown Interventions , author=

  114. [122]

    arXiv preprint arXiv:2302.03248 , year=

    Disentangled Causal Embedding With Contrastive Learning For Recommender System , author=. arXiv preprint arXiv:2302.03248 , year=

    arXiv

  115. [123]

    Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

    Causal Attention for Interpretable and Generalizable Graph Classification , author=. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

  116. [124]

    npj Digital Medicine , volume=

    Shortcut learning in medical AI hinders generalization: method for estimating AI model generalization without external data , author=. npj Digital Medicine , volume=. 2024 , publisher=

    2024

  117. [125]

    arXiv preprint arXiv:1903.03894 , year=

    Gnn explainer: A tool for post-hoc explanation of graph neural networks , author=. arXiv preprint arXiv:1903.03894 , year=

    1903 arXiv

  118. [126]

    Advances in neural information processing systems , volume=

    Parameterized explainer for graph neural network , author=. Advances in neural information processing systems , volume=

  119. [127]

    International Conference on Machine Learning , pages=

    Generative causal explanations for graph neural networks , author=. International Conference on Machine Learning , pages=. 2021 , organization=

    2021

  120. [128]

    Advances in Neural Information Processing Systems , volume=

    Cxplain: Causal explanations for model interpretation under uncertainty , author=. Advances in Neural Information Processing Systems , volume=

  121. [129]

    International Conference on Learning Representations , year=

    Handling Distribution Shifts on Graphs: An Invariance Perspective , author=. International Conference on Learning Representations , year=

  122. [130]

    Design and Applications , volume=

    Recurrent neural networks , author=. Design and Applications , volume=

  123. [131]

    arXiv preprint arXiv:2305.12788 , year=

    GraphCare: Enhancing Healthcare Predictions with Open-World Personalized Knowledge Graphs , author=. arXiv preprint arXiv:2305.12788 , year=

    arXiv

  124. [132]

    Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , pages=

    Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks , author=. Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , pages=

  125. [133]

    Neural computation , volume=

    Long short-term memory , author=. Neural computation , volume=. 1997 , publisher=

    1997

  126. [134]

    Advances in neural information processing systems , volume=

    Mime: Multilevel medical embedding of electronic health records for predictive healthcare , author=. Advances in neural information processing systems , volume=

  127. [135]

    2020 IEEE International Conference on Big Data (Big Data) , pages=

    Heterogeneous similarity graph neural network on electronic health records , author=. 2020 IEEE International Conference on Big Data (Big Data) , pages=. 2020 , organization=

    2020

  128. [136]

    Proceedings of the 27th ACM International Conference on Information and Knowledge Management , pages=

    Kame: Knowledge-based attention model for diagnosis prediction in healthcare , author=. Proceedings of the 27th ACM International Conference on Information and Knowledge Management , pages=

  129. [137]

    IEEE journal of biomedical and health informatics , volume=

    Deepr: a convolutional net for medical records , author=. IEEE journal of biomedical and health informatics , volume=. 2016 , publisher=

    2016

  130. [138]

    Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , pages=

    GRAM: graph-based attention model for healthcare representation learning , author=. Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining , pages=

  131. [139]

    arXiv preprint arXiv:2105.02711 , year=

    Safedrug: Dual molecular graph encoders for recommending effective and safe drug combinations , author=. arXiv preprint arXiv:2105.02711 , year=

    arXiv

  132. [140]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Concare: Personalized clinical feature embedding via capturing the healthcare context , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  133. [141]

    Proceedings of the ACM Web Conference 2023 , pages=

    MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning , author=. Proceedings of the ACM Web Conference 2023 , pages=

    2023

  134. [142]

    proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Gamenet: Graph augmented memory networks for recommending medication combination , author=. proceedings of the AAAI Conference on Artificial Intelligence , volume=

  135. [143]

    30th International Joint Conference on Artificial Intelligence, IJCAI 2021 , pages=

    Change Matters: Medication Change Prediction with Recurrent Residual Networks , author=. 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 , pages=. 2021 , organization=

    2021

  136. [144]

    Proceedings of The Web Conference 2020 , pages=

    Stagenet: Stage-aware neural networks for health risk prediction , author=. Proceedings of The Web Conference 2020 , pages=

    2020

  137. [145]

    Agent: Clinical predictive model via mimicked second opinions , author=

    Dr. Agent: Clinical predictive model via mimicked second opinions , author=. Journal of the American Medical Informatics Association , volume=. 2020 , publisher=

    2020

  138. [146]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Adacare: Explainable clinical health status representation learning via scale-adaptive feature extraction and recalibration , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  139. [147]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    GRASP: generic framework for health status representation learning based on incorporating knowledge from similar patients , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  140. [148]

    Neurology , volume=

    Development of expert-level classification of seizures and rhythmic and periodic patterns during eeg interpretation , author=. Neurology , volume=. 2023 , publisher=

    2023

  141. [149]

    Proceedings of the Conference on Health, Inference, and Learning , pages=

    Variationally regularized graph-based representation learning for electronic health records , author=. Proceedings of the Conference on Health, Inference, and Learning , pages=

  142. [150]

    Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) , pages=

    Hypergraph contrastive learning for electronic health records , author=. Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) , pages=. 2022 , organization=

    2022

  143. [151]

    and Ver Steeg, Greg and Galstyan, Aram , title=

    Harutyunyan, Hrayr and Khachatrian, Hrant and Kale, David C. and Ver Steeg, Greg and Galstyan, Aram , title=. Scientific Data , year=. doi:10.1038/s41597-019-0103-9 , url=

    doi

  144. [152]

    Advances in neural information processing systems , volume=

    Multi-task learning as multi-objective optimization , author=. Advances in neural information processing systems , volume=

  145. [153]

    5th International Conference on Learning Representations Workshop , year=

    Trace Norm Regularised Deep Multi-Task Learning , author=. 5th International Conference on Learning Representations Workshop , year=

  146. [154]

    , author=

    Multi-Task Learning for Classification with Dirichlet Process Priors. , author=. Journal of Machine Learning Research , volume=

  147. [155]

    Advances in neural information processing systems , volume=

    Learning multiple tasks with multilinear relationship networks , author=. Advances in neural information processing systems , volume=

  148. [156]

    Multi-task Sequence to Sequence Learning , author=

  149. [157]

    Multi-task learning for multiple language translation , author=. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , pages=

  150. [158]

    , author=

    Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. , author=

  151. [159]

    arXiv preprint arXiv:2101.04209 , year=

    Pyhealth: A python library for health predictive models , author=. arXiv preprint arXiv:2101.04209 , year=

    arXiv

  152. [160]

    Proceedings of the Biennial GSCL Conference , pages =

    Normalized (Pointwise) Mutual Information in Collocation Extraction , author =. Proceedings of the Biennial GSCL Conference , pages =

  153. [161]

    Proceedings of the 2nd clinical natural language processing workshop , pages=

    Publicly available clinical BERT embeddings , author=. Proceedings of the 2nd clinical natural language processing workshop , pages=

  154. [162]

    arXiv preprint arXiv:2308.12029 , year =

    Dual-Balancing for Multi-Task Learning , author =. arXiv preprint arXiv:2308.12029 , year =

  155. [163]

    Journal of the American Medical Informatics Association , volume=

    Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review , author=. Journal of the American Medical Informatics Association , volume=. 2018 , publisher=

    2018

  156. [164]

    NPJ digital medicine , volume=

    Scalable and accurate deep learning with electronic health records , author=. NPJ digital medicine , volume=. 2018 , publisher=

    2018

  157. [165]

    IEEE journal of biomedical and health informatics , volume=

    Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis , author=. IEEE journal of biomedical and health informatics , volume=. 2017 , publisher=

    2017

  158. [166]

    Proceedings of the 2016 SIAM international conference on data mining , pages=

    Risk prediction with electronic health records: A deep learning approach , author=. Proceedings of the 2016 SIAM international conference on data mining , pages=. 2016 , organization=

    2016

  159. [167]

    IEEE access , volume=

    A systematic review of graph neural network in healthcare-based applications: Recent advances, trends, and future directions , author=. IEEE access , volume=. 2024 , publisher=

    2024

  160. [168]

    Journal of biomedical informatics , volume=

    Adaptable graph neural networks design to support generalizability for clinical event prediction , author=. Journal of biomedical informatics , volume=. 2025 , publisher=

    2025

  161. [169]

    ACM Transactions on Computing for Healthcare , volume=

    Self-supervised representation learning on electronic health records with graph kernel infomax , author=. ACM Transactions on Computing for Healthcare , volume=. 2024 , publisher=

    2024

  162. [170]

    Scientific reports , volume=

    Leveraging graph-based hierarchical medical entity embedding for healthcare applications , author=. Scientific reports , volume=. 2021 , publisher=

    2021

  163. [171]

    Journal of biomedical informatics , volume=

    Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study , author=. Journal of biomedical informatics , volume=. 2023 , publisher=

    2023

  164. [172]

    Artificial intelligence in medicine , volume=

    Real-world data medical knowledge graph: construction and applications , author=. Artificial intelligence in medicine , volume=. 2020 , publisher=

    2020

  165. [173]

    Advances in Neural Information Processing Systems , volume=

    Automated multi-task learning for joint disease prediction on electronic health records , author=. Advances in Neural Information Processing Systems , volume=

  166. [174]

    IEEE Journal of Biomedical and Health Informatics , volume=

    Genhpf: General healthcare predictive framework for multi-task multi-source learning , author=. IEEE Journal of Biomedical and Health Informatics , volume=. 2023 , publisher=

    2023

  167. [175]

    Scientific data , volume=

    MIMIC-III, a freely accessible critical care database , author=. Scientific data , volume=. 2016 , publisher=

    2016

  168. [176]

    Scientific data , volume=

    MIMIC-IV, a freely accessible electronic health record dataset , author=. Scientific data , volume=. 2023 , publisher=

    2023

  169. [177]

    arXiv preprint arXiv:1909.01315 , year=

    Deep graph library: A graph-centric, highly-performant package for graph neural networks , author=. arXiv preprint arXiv:1909.01315 , year=

    1909 arXiv

  170. [178]

    Machine learning for healthcare conference , pages=

    Doctor ai: Predicting clinical events via recurrent neural networks , author=. Machine learning for healthcare conference , pages=. 2016 , organization=

    2016

  171. [179]

    NPJ digital medicine , volume=

    Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction , author=. NPJ digital medicine , volume=. 2021 , publisher=

    2021

  172. [180]

    2019 , note =

    Clinical Classifications Software (. 2019 , note =

    2019

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.