Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks

Dhanesh Ramachandram; Sara Ketabi

REVIEW 4 major objections 5 minor 1 cited by

Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read By aligning static EHR features with discharge summaries through a CLIP-style contrastive loss, the paper shows that the pretrained EHR encoder alone beats XGBoost on 30-day readmission (0.809 vs 0.777 AUROC) and critical outcome…

desk verdict A clean CLIP-style extension for static EHR + discharge summaries, but the critical-outcome gain likely leaks through outcome-bearing text; needs a text-probe before it's believable. read the letter →

arxiv 2505.17643 v1 pith:25OREWG3 submitted 2025-05-23 cs.CL cs.LG

Sara Ketabi , Dhanesh Ramachandram This is my paper

classification cs.CLcs.LG

keywords contrastivelearningelectronichealthrecordsdischargesummariesmultimodalrepresentationCLIPlossTabNetreadmissionpredictionclinicaldecisionsupport

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

The paper tries to show that pairing plain structured electronic health records (EHR) with discharge summaries during a contrastive pretraining step makes the EHR representation good enough to predict outcomes that tabular-only models handle poorly. The pretraining pulls the embedding of a patient's EHR close to the embedding of that same patient's discharge summary, and pushes different patients' pairs apart, using a CLIP-style loss. After fine-tuning only the EHR encoder on labeled data, the paper reports AUROC of 0.809 for 30-day readmission and 0.821 for critical outcomes, outperforming XGBoost (0.777 and 0.725) and a masked-pretrained TabNet baseline. The practical point is that clinical notes never have to be available at prediction time: they contribute their knowledge during pretraining, then the EHR-only model carries it forward.

What carries the argument

The load-bearing object is the contrastive alignment itself: a CLIP-style loss over paired EHR and text embeddings, computed as the sum of EHR-to-text and text-to-EHR InfoNCE terms with a temperature parameter. The EHR side uses a TabNet encoder, first pretrained with masked feature reconstruction and then with its low-level embedding and feature-splitting layers frozen; the text side uses a Longformer encoder initialized with clinical-domain weights, processing discharge notes in 256-token chunks and mean-pooling the [CLS] embeddings. Two small MLP projection heads map both modalities into the shared 128-dimensional space where cosine similarity is compared. What this mechanism does is let the structured representation inherit information from the unstructured note during pretraining, while remaining purely tabular at inference.

What would settle it

Train the same contrastive framework on discharge summaries from which all sentences mentioning death, critical events, or readmission are removed; if the downstream AUROC gains over masked-pretrained TabNet shrink or vanish, the effect is label leakage, not semantic transfer.

Watch

Extended reading notes

Core claim

The central discovery is that cross-modal alignment of static EHR features with free-text discharge summaries transfers clinically meaningful signal into the EHR encoder. Concretely, a TabNet encoder pretrained with a contrastive objective—matching the embedding of each admission's structured record to its discharge summary while repelling unpaired records—then fine-tuned on two binary tasks reaches mean test AUROC 0.809 for 30-day readmission and 0.821 for critical outcome prediction. These numbers beat the same TabNet architecture initialized only with masked pretraining (0.759 and 0.745) and beat XGBoost (0.777 and 0.725). The gains persist when training data is cut in half, and the text modality is used neither during fine-tuning nor at inference; the paper attributes the improvement to semantic knowledge absorbed from discharge summaries.

Load-bearing premise

The discharge summaries used during contrastive pretraining must not themselves reveal whether the patient was readmitted, died, or had a critical event; otherwise the reported improvement would measure information leakage rather than understanding transferred from text.

Editorial extensions

If this is right

An EHR-only model can absorb knowledge from clinical notes during pretraining and then make predictions without needing notes at inference, making deployment cheaper in settings where notes are unavailable downstream.
The 50%-data experiments imply the pretrained initialization preserves most of its advantage under data scarcity: with 3,000 training points it still beats baselines trained on the full set.
Static-form EHR is sufficient for cross-modal contrastive pretraining, so hospitals without longitudinal or time-series records can still use the approach.
Because the framework is modality-agnostic, the same alignment recipe can in principle be applied to other paired structured and unstructured clinical data, such as imaging metadata and radiology reports.
The method does not require task-specific pretraining labels; the alignment is self-supervised using naturally paired admission records and discharge summaries.

Reading between the lines

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

A decisive check the paper leaves implicit is whether shuffling or paraphrasing the notes while keeping outcome-related content would preserve the gain; if it would, the mechanism is coarse label leakage rather than fine-grained semantic alignment.
The selection-bias caveat the paper acknowledges implies that the pretraining population (patients with discharge summaries) and the fine-tuning population (patients without notes) may differ, so applying the framework where note availability correlates with severity could overstate or understate the transfer benefit.
The same architecture could be extended to predict multiple outcomes at once, or to use the aligned text encoder for report generation from EHR, which the paper names as future work but leaves untested.
One could test whether the alignment transfers across hospitals and domains; the paper uses a single dataset, so cross-institution generalizability remains open.

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.

Desk Editor's Note

read the letter

Thanks for the report. I read the paper carefully and mostly agree with your conditional verdict, with one adjustment: I think the leakage risk is even more central than your report suggests, and I'd want a text-probe experiment before believing the critical outcome numbers.

What's actually new: the paper is the first I've seen that applies a standard CLIP-style contrastive loss to static (non-temporal) EHR feature vectors and discharge summaries, then fine-tunes only the EHR encoder for downstream tasks. That's a useful extension, especially for settings where longitudinal sequences aren't available. The work is cleanly presented, the baselines (XGBoost and masked-pretrained TabNet) are appropriate, and the low-resource result—CL-pretrained TabNet at 50% data beating XGBoost at 100%—is genuinely interesting. The authors also honestly acknowledge selection bias and the two-task scope in their limitations.

The soft spots are real. The critical outcome result (AUC 0.821 vs 0.725) is large enough to be suspicious. Discharge summaries in MIMIC-IV routinely state outcomes: 'the patient expired,' 'comfort care,' 'DNR,' 'discharged to hospice.' The preprocessing strips numbers, dates and punctuation but not outcome-bearing sentences. The CLIP loss will happily align that signal with the EHR embedding, so the downstream EHR encoder has a shortcut to the label. The paper excludes target-related structured columns but never addresses outcome text. That's a load-bearing gap. A simple experiment—train a linear probe on the text embeddings to predict the outcome—would settle whether the info is in the notes. Similarly, the readmission gain may be inflated if summaries mention planned returns or discharge disposition, though it's smaller and more plausible as genuine transfer.

Second issue: the fine-tuning set is deliberately patients without discharge summaries, while pretraining used patients with them. The authors call this a leakage safeguard, but it's actually a distribution shift. Note-availability may correlate with severity and outcomes, and the pretrained encoder is being tested on a population it never saw. This could over- or under-estimate the transfer; it's not a clean evaluation.

Third, no code, data, or weights are actually available despite the claim in Section 4.2. The supplementary material is not accessible in the arXiv submission. For a reproducibility-relevant claim, that's a problem.

Overall: this is a competent, honest paper that makes a narrow but legitimate contribution. The readmission result is credible. The critical outcome result is not, without a leakage check. It deserves a serious referee, but I'd recommend rejection unless the authors add a text-probe analysis, address the population shift, and release artifacts.

Referee Report

4 major / 5 minor

Summary. The paper introduces a multimodal contrastive learning framework that aligns static EHR features with discharge summary text via a CLIP-style loss, then fine-tunes the EHR encoder on two downstream tasks: 30-day hospital readmission and critical outcome prediction. The authors report that the CL-pretrained TabNet encoder achieves mean test AUCs of 0.809 and 0.821, outperforming XGBoost and a masked-pretraining-only TabNet baseline, and that the gains persist when fine-tuning on 50% of the training data. The central claim is that contrastive pretraining transfers clinically meaningful semantic information from clinical notes into the EHR encoder, enabling text-free inference. The paper includes code and pretrained weights in the supplementary material and reports statistical significance across five random seeds.

Significance. If the central claim holds, the framework would be a practical way to inject clinical knowledge from notes into tabular-EHR pipelines without requiring text at inference, which is attractive for deployment settings where text is unavailable or expensive. The paper's strengths include a clear and reproducible architecture, paired-data construction from MIMIC-IV, multiple-seed evaluation with significance tests, and public release of code and weights. However, the empirical claims rest on a load-bearing assumption that the discharge summaries carry no direct information about the downstream labels. Because MIMIC-IV discharge summaries frequently contain explicit outcome statements, such as 'the patient expired' or 'comfort care', the reported gains, especially the 13.25% AUROC improvement for critical outcome prediction, may partly or wholly reflect label leakage through the contrastive objective rather than general semantic transfer. The second load-bearing assumption, that the with-notes pretraining population is representative of the without-notes fine-tuning population, is acknowledged as a limitation but not quantified or corrected.

major comments (4)

The text preprocessing in §4.1 removes dates, numbers, punctuation, and some administrative sections, but it does not remove outcome-bearing sentences from discharge summaries. MIMIC-IV discharge summaries often contain explicit statements about death, code status, and critical events (e.g., 'the patient expired', 'DNR', 'comfort care'). Because the CLIP loss in §3.1 aligns EHR embeddings with text embeddings, any such outcome signal in the text is pulled into the EHR encoder. At fine-tuning time, the EHR encoder can then exploit this shortcut, making the critical-outcome gain in Table 2 (13.25% over XGBoost) a potential measure of label leakage rather than semantic transfer. The paper's statement that text is not used at inference does not address leakage during pretraining. I recommend a direct probe experiment: train a linear classifier on the frozen EHR embeddings (or frozen text embeddings) to predict each downstream label and compare against chance; additionally, retrain the CL framework after removing outcome-related sections or sentences (e.g., 'Discharge Disposition', 'Hospital Course' sentences mentioning death/code status) and report whether the downstream gains persist. This experiment is essential because the entire empirical claim rests on the text modality not encoding the labels.
The contrastive pretraining uses 376,021 admissions with discharge summaries, while downstream fine-tuning and testing use a held-out portion of admissions without discharge summaries. The Discussion acknowledges that patients without notes may differ systematically in clinically relevant ways, but the paper does not quantify this selection bias or test its impact. This matters because the claim is that the CL-pretrained encoder 'transfers' knowledge to the note-free population; if the two populations differ in outcome prevalence or comorbidity, part of the observed AUC gain could be due to distribution shift rather than semantic transfer. I suggest reporting patient characteristics for both populations (age, sex, comorbidity counts, outcome rates), and either matching the fine-tuning population to the pretraining population on observable covariates or reporting results on both a with-notes held-out set and the without-notes set. This would clarify whether the fine-tuning improvements generalize beyond the specific population used for evaluation.
The paper reports p-values from two-tailed t-tests across five seeds, which is appropriate, but the test is underpowered with only five seeds and the standard deviations overlap across baselines in several cases. For example, in Table 1 the 100%-data XGBoost AUC is 0.777 ± 0.012 and the CL-TabNet AUC is 0.809 ± 0.016; the t-test is significant only because the paired seeds are correlated. I recommend reporting the full per-seed results in the main text (or at least the paired differences) and also computing a non-parametric test (e.g., Wilcoxon signed-rank) to confirm the significance claim. This is a minor statistical robustness request, but it directly concerns the confidence one can place in the headline improvements.
The paper evaluates on a single dataset (MIMIC-IV) and two binary classification tasks. The authors acknowledge this in the Discussion and list external validation as future work, which is good, but the manuscript's title and framing ('Enhanced Clinical Tasks') suggest broader applicability. For a clinical prediction claim, at least one external or temporal validation would substantially strengthen the paper. I would not make this a blocker if the leakage and population-matching issues are resolved, but it is a limitation that should be stated more prominently in the abstract or conclusion, not only in the future-work paragraph.

minor comments (5)

There is a typo in the abstract: 'e.g,' should be 'e.g.,'. Also, in §3.1 the phrase 'As shown by Chen et al. (2020)' should be 'as shown by Chen et al. (2020)' (lowercase 'as') for sentence flow.
The reference for NODE is listed as 'Popov et al. (1909)' but the correct year is 2019; the arXiv number confirms this. Please correct the citation year.
The phrase 'and and broadens the applicability' contains a duplicated 'and'. Also, 'Tabtransformer' in §2.1 should be 'TabTransformer' with a capital T for the architecture name as used in the cited paper.
The evaluation setting section does not state the class imbalance or the prevalence of the positive class for either downstream task. Reporting prevalence is standard for clinical prediction papers and helps readers interpret AUC values. Please add this information.
The figures are informative but the text labels are small and some are cut off in the PDF rendering. Please ensure the figures are legible and that all text in the figures is readable at print size.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the downstream gains are empirical results from fine-tuning on held-out data, not a derivation equivalent to the pretraining inputs.

full rationale

The paper's central claim is an empirical one: initializing a TabNet EHR encoder with weights learned from a CLIP-style EHR-text alignment improves held-out AUROC for 30-day readmission and critical outcome prediction. Nothing in the paper's equations defines the downstream predictions in terms of the contrastive objective or the discharge summaries at inference time; the CLIP loss only shapes the pretrained embedding, and the downstream classifier is separately trained with binary cross-entropy on labeled data. The baselines include a TabNet initialized with masked pretraining only, which isolates the effect of the contrastive stage. The paper also states that downstream fine-tuning uses a held-out portion of data without discharge notes, so the evaluated cases were not in the contrastive pretraining set. The acknowledged concerns are generalization and potential label leakage if discharge summaries mention outcomes, but leakage is a validity threat rather than circularity: no reported equation or construction forces the improved AUROC to equal a quantity already contained in the pretraining inputs. The use of Clinical Longformer weights pretrained on MIMIC-III notes and evaluation on MIMIC-IV is a corpus-overlap / generalization caveat, not a self-citation or definitional reduction. The paper's own Section 5 limitations candidly note the selection-bias risk from relying on notes, which further indicates the authors do not present the result as a tautology. Overall, the claimed derivation chain is self-contained and empirically testable, so the circularity score is 0.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

No new physical or conceptual entities are introduced. The central result rests on standard ML components and on domain assumptions about note content, population exchangeability, and feature construction; the strongest hidden assumption is that notes do not leak outcome labels.

free parameters (6)

CLIP temperature tau = 0.1
Chosen by hand in Section 4.2; controls the sharpness of the contrastive similarity distribution and affects alignment quality. No sensitivity analysis is provided.
Text chunk size = 256 tokens
Discharge notes are divided into 256-token chunks in Section 3.1; this arbitrary choice affects the mean-pooled text representation.
Projection dimension = 128
MLP projection heads map both modalities to 128 dimensions in Section 3.1; the dimension is chosen to match the TabNet encoder output and is not justified.
Frozen layer split = Longformer first 10 layers frozen; TabNet embedding and splitting layers frozen
Compute-saving choice described in Sections 3.1 and 3.2; no ablation shows that this split is optimal.
Downstream epochs and batch size = 15 epochs, batch size 64
Section 4.2 lists these values without a tuning study or sensitivity analysis.
Learning rates and weight decay = LR 1e-4 pretrain, 5e-4 fine-tune; WD 1e-4
Section 4.2 gives chosen hyperparameters; no evidence that they are optimal or robust.

assumptions (5)

domain assumption Paired EHR and discharge summary from the same admission share enough semantic information that aligning their embeddings improves EHR representations for downstream tasks.
Core premise of the CL framework in Section 3.1; if notes add no task-relevant signal beyond structured features, the method has nothing to transfer.
domain assumption Discharge summaries do not encode the downstream labels, specifically 30-day readmission and critical outcome.
Load-bearing for a clean evaluation; Section 4.1 removes target columns from EHR but does not scrub outcome-related text, so for critical outcome or mortality the assumption may fail and leak labels into the EHR encoder.
domain assumption Patients with discharge notes, used for pretraining, and patients without notes, used for fine-tuning, are drawn from the same distribution.
Section 4.1 uses no-note admissions for fine-tuning; the authors acknowledge this as a selection bias in the Discussion. If the populations differ clinically, the transfer results may not generalize.
domain assumption The 105-feature static EHR representation built from MIMIC-IV is a faithful, leakage-free encoding of the admission.
Feature construction is only summarized in Section 4.1; unstated choices about column selection, ordinal encoding, and missing-value exclusion can affect results.
domain assumption Clinical Longformer weights pretrained on MIMIC-III notes transfer to MIMIC-IV discharge summaries.
Section 3.1 initializes the text encoder from Clinical Longformer; no validation shows that domain shift across MIMIC versions is negligible.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks." pith.science (2026). https://pith.science/paper/25OREWG3

@misc{pith2026250517643,
  author       = {Pith},
  title        = {Pith review of: Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/25OREWG3}},
  note         = {Machine review of arXiv:2505.17643}
}

read the original abstract

Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction tasks using electronic health record (EHR) data. Despite their strengths, these models struggle with tasks that require deeper contextual understanding, such as predicting 30-day hospital readmission. This can be primarily due to the limited semantic information available in structured EHR data. To address this limitation, we propose a deep multimodal contrastive learning (CL) framework that aligns the latent representations of structured EHR data with unstructured discharge summary notes. It works by pulling together paired EHR and text embeddings while pushing apart unpaired ones. Fine-tuning the pretrained EHR encoder extracted from this framework significantly boosts downstream task performance, e.g., a 4.1% AUROC enhancement over XGBoost for 30-day readmission prediction. Such results demonstrate the effect of integrating domain knowledge from clinical notes into EHR-based pipelines, enabling more accurate and context-aware clinical decision support systems.

Figures

Figures reproduced from arXiv: 2505.17643 by the authors.

**Figure 1.** To encode the structured EHR inputs Xehr ∈ R d , where d is the number of features, and get EHR representations, we employed the encoder portion of TabNet. As discussed in Section 2, TabNet utilizes a sequential attention mechanism and sparse feature selection to extract complex interactions within the tabular data. Let e be the EHR representation and T indicate an instance of TabNet: e = T(Xehr) ∈ R 128 (1) Followi… view at source ↗

**Figure 1.** The Proposed EHR-Text CL Framework: EHR is encoded with TabNet initialized with [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

**Figure 2.** The Downstream Classification Architecture: The TabNet encoder is initialized with the [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction
cs.LG 2026-04 unverdicted novelty 6.0 of 10

Ontology-aware contrastive pretraining plus note-to-vitals distillation improves MIMIC ICU risk and length-of-stay prediction using only vital signs at inference.

Reference graph

Works this paper leans on

18 extracted references · 10 canonical work pages · cited by 1 Pith paper

[1]

Tabnet: Attentive interpretable tabular learning

Sercan \"O Arik and Tomas Pfister. Tabnet: Attentive interpretable tabular learning. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 6679--6687, 2021

work page 2021
[2]

Longformer: The long-document transformer

Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020

arXiv 2004
[3]

A simple framework for contrastive learning of visual representations

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597--1607. PmLR, 2020

2020
[4]

Best of both worlds: Multimodal contrastive learning with tabular and imaging data

Paul Hager, Martin J Menten, and Daniel Rueckert. Best of both worlds: Multimodal contrastive learning with tabular and imaging data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23924--23935, 2023

work page 2023
[5]

Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning

Steven Horng, David A Sontag, Yoni Halpern, Yacine Jernite, Nathan I Shapiro, and Larry A Nathanson. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PloS one, 12 0 (4): 0 e0174708, 2017

work page 2017
[6]

Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition

Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3942--3951, 2021

work page 2021
[7]

Tabtransformer: Tabular data modeling using contextual embeddings

Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin. Tabtransformer: Tabular data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678, 2020

arXiv 2012
[8]

Improving joint learning of chest x-ray and radiology report by word region alignment

Zhanghexuan Ji, Mohammad Abuzar Shaikh, Dana Moukheiber, Sargur N Srihari, Yifan Peng, and Mingchen Gao. Improving joint learning of chest x-ray and radiology report by word region alignment. In Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceed...

work page 2021

Show all 18 references

[9]

Mimic-iv, a freely accessible electronic health record dataset

Alistair EW Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, Steven Horng, Tom J Pollard, Sicheng Hao, Benjamin Moody, Brian Gow, et al. Mimic-iv, a freely accessible electronic health record dataset. Scientific data, 10 0 (1): 0 1, 2023

2023
[10]

Multimodal pretraining of medical time series and notes

Ryan King, Tianbao Yang, and Bobak J Mortazavi. Multimodal pretraining of medical time series and notes. In Machine Learning for Health (ML4H), pages 244--255. PMLR, 2023

2023
[11]

Fuyuan Li, Zhanjin Wang, Ruiling Bian, Zhangtuo Xue, Junjie Cai, Ying Zhou, and Zhan Wang. Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the m...

2025
[12]

Clinical-longformer and clinical-bigbird: Transformers for long clinical sequences

Yikuan Li, Ramsey M Wehbe, Faraz S Ahmad, Hanyin Wang, and Yuan Luo. Clinical-longformer and clinical-bigbird: Transformers for long clinical sequences. arXiv preprint arXiv:2201.11838, 2022

2022 arXiv
[13]

Development and validation of machine learning models to predict mdro colonization or infection on icu admission by using electronic health record data

Yun Li, Yuan Cao, Min Wang, Lu Wang, Yiqi Wu, Yuan Fang, Yan Zhao, Yong Fan, Xiaoli Liu, Hong Liang, et al. Development and validation of machine learning models to predict mdro colonization or infection on icu admission by using electronic health record data. Antimicrobial Re...

2024
[14]

Machine learning for the prediction of delirium in elderly intensive care unit patients

Rui Ma, Jin Zhao, Ziying Wen, Yunlong Qin, Zixian Yu, Jinguo Yuan, Yumeng Zhang, Anjing Wang, Cui Li, Huan Li, et al. Machine learning for the prediction of delirium in elderly intensive care unit patients. European Geriatric Medicine, 15 0 (5): 0 1393--1403, 2024 a

2024
[15]

Global contrastive training for multimodal electronic health records with language supervision

Yingbo Ma, Suraj Kolla, Zhenhong Hu, Dhruv Kaliraman, Victoria Nolan, Ziyuan Guan, Yuanfang Ren, Brooke Armfield, Tezcan Ozrazgat-Baslanti, Jeremy A Balch, et al. Global contrastive training for multimodal electronic health records with language supervision. arXiv preprint arX...

2024 arXiv
[16]

Neural oblivious decision ensembles for deep learning on tabular data

Sergei Popov, Stanislav Morozov, and Artem Babenko. Neural oblivious decision ensembles for deep learning on tabular data. arxiv 2019. arXiv preprint arXiv:1909.06312, 1909

2019 arXiv
[17]

Learning transferable visual models from natural language supervision

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pa...

2021
[18]

Multi-granularity cross-modal alignment for generalized medical visual representation learning

Fuying Wang, Yuyin Zhou, Shujun Wang, Varut Vardhanabhuti, and Lequan Yu. Multi-granularity cross-modal alignment for generalized medical visual representation learning. Advances in Neural Information Processing Systems, 35: 0 33536--33549, 2022

2022

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Tabnet: Attentive interpretable tabular learning

Sercan \"O Arik and Tomas Pfister. Tabnet: Attentive interpretable tabular learning. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 6679--6687, 2021

work page 2021

[2] [2]

Longformer: The long-document transformer

Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020

arXiv 2004

[3] [3]

A simple framework for contrastive learning of visual representations

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597--1607. PmLR, 2020

2020

[4] [4]

Best of both worlds: Multimodal contrastive learning with tabular and imaging data

Paul Hager, Martin J Menten, and Daniel Rueckert. Best of both worlds: Multimodal contrastive learning with tabular and imaging data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23924--23935, 2023

work page 2023

[5] [5]

Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning

Steven Horng, David A Sontag, Yoni Halpern, Yacine Jernite, Nathan I Shapiro, and Larry A Nathanson. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PloS one, 12 0 (4): 0 e0174708, 2017

work page 2017

[6] [6]

Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition

Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3942--3951, 2021

work page 2021

[7] [7]

Tabtransformer: Tabular data modeling using contextual embeddings

Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin. Tabtransformer: Tabular data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678, 2020

arXiv 2012

[8] [8]

Improving joint learning of chest x-ray and radiology report by word region alignment

Zhanghexuan Ji, Mohammad Abuzar Shaikh, Dana Moukheiber, Sargur N Srihari, Yifan Peng, and Mingchen Gao. Improving joint learning of chest x-ray and radiology report by word region alignment. In Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceed...

work page 2021