Pith. sign in

REVIEW 3 major objections 4 minor 1 cited by

EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records

T0 review · 3 major / 4 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 A single LLM predicts next-hour hospital states across ED, ward, and ICU, and rolls them into full simulated patient pathways.

desk verdict A useful architectural idea—the Masked Summarization Bottleneck—wrapped in an abstract that overclaims full-stay simulation when every task is capped at 24 hours or 3 days. read the letter →

arxiv 2506.04831 v3 pith:MVWKK6BQ submitted 2025-06-05 cs.LG cs.CL

Chantal Pellegrini , Ege Özsoy , David Bani-Harouni , Matthias Keicher , Nassir Navab This is my paper
classification cs.LGcs.CL MSC 68T0792C50
keywords electronichealthrecordspatientpathwaysimulationtrajectoryforecastinglargelanguagemodelsmaskedsummarizationbottleneckMIMIC-IVnext-hourstatepredictioniterative
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

EHR2Path tries to establish that a single language-model-based system can forecast and simulate a patient's entire in-hospital course — emergency department, ward, and ICU — from the full breadth of routine electronic health record data, including free-text notes, vital signs, lab values, medications, and dense bedside charting. The point is to move beyond isolated outcome classifiers toward a pathway-level model that predicts the next hour's state and can be rolled forward to simulate multi-hour or multi-day trajectories. The paper reports that this works on MIMIC-IV, with the summary-based variant matching or beating text-only models while using far fewer tokens, and with fine-tuned variants outperforming strong outcome-prediction baselines on several tasks. A sympathetic reader would take the central claim to be that routine, noisy, heterogeneous EHR data are sufficient for pathway-level forecasting if the history is compressed well.

What carries the argument

The central mechanism is the Masked Summarization Bottleneck, a custom attention mask appended to an LLM. For each section of the patient record, a sequence of input tokens is followed by m summary tokens and then output tokens; the mask lets output tokens attend only to the summary tokens, forcing the model to write the information needed for next-hour prediction into m compact embeddings during training. Because the summaries are optimized to predict the next state rather than reconstruct the input, they act as a task-optimized lossy compression of the full history. This is paired with a hierarchical text representation (clinical units such as ED, hospital, and ICU; categories such as vitals or medications; individual features), where identical consecutive values are merged into intervals and missing data simply absent, and with a Length-of-Stay countdown token that is dropped or noised during training and re-estimated at inference so simulations converge to discharge.

What would settle it

On a cohort of patients who experience rapid deterioration (e.g., an unplanned ICU transfer within six hours of ED vitals), compare the model's next-hour vital-sign forecasts against raw chart data at five-minute resolution: if the model's error on the hour preceding deterioration is no worse than on stable hours, the hourly bottleneck is safe; if it systematically misses the deterioration by predicting near-baseline values, the representation is lossy.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that next-hour EHR state prediction over a very broad feature space — with outputs covering vitals, labs, medications, procedures, chart events, and care transitions such as admission, discharge, and death — is learnable by an LLM, and that iterating this prediction produces plausible full-pathway simulations. The Masked Summarization Bottleneck is what makes this feasible: it compresses each section of a patient's history into a handful of learned summary tokens, so the model can attend to an average of ~9,800 tokens of context using only ~220 input tokens in the summary-only variant, roughly 20 times more history than a 24-hour text window. In the reported experiments the combined summary-plus-text model is the most stable, ranking first or second across simulation tasks, and it outperforms the ETHOS baseline on the shared task of hospital lab-value development. Fine-tuned for outcomes, the model exceeds the specialized baselines MEME and REMed on three of four tasks, including ICU imminent mortality (0.83 accuracy vs. 0.71).

Load-bearing premise

The load-bearing premise is that aggregating raw EHR events into hourly averages and most-frequent categorical values, then serializing them as text, preserves the acute clinical changes a pathway model needs to predict; if sub-hourly deterioration is averaged away, the forecasts will be smooth but misleading.

Editorial extensions

If this is right

  • A single model can replace a collection of narrow task-specific predictors, since next-hour forecasting over all features subsumes tasks like vital-sign, lab, medication, and input development.
  • Iterative rollout enables multi-day simulation of in-hospital trajectories, which could be used to generate what-if scenarios for treatment planning and discharge timing.
  • Up to 20x more historical context can be supplied without proportional token growth, reducing cost for long-stay patients.
  • Outcome-oriented fine-tuning turns the same pathway model into a competitive foundation model for tasks like imminent mortality and imminent ICU discharge.
  • Prediction quality degrades gradually rather than catastrophically over a 24-hour horizon, suggesting the simulated trajectories remain informative for clinical use.

Reading between the lines

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

  • Because the bottleneck is trained to optimize next-hour prediction, the summary tokens are not guaranteed to preserve information relevant to other uses, such as long-horizon risk scoring; a model wanting both may need multiple summary sets or a multi-task training objective.
  • The hourly aggregation step averages numerical values and takes the most frequent categorical value. If clinically significant sub-hourly deterioration is washed out, the simulated trajectories could look smooth while missing acute events; a test on high-resolution bedside chart data would quantify this.
  • The LOS countdown is learned as a number, not derived from clinical triggers. In deployment, where the true remaining stay is unknown, the model must rely on its own predicted LOS, and errors could compound across rolls.
  • The same text-serialization and summarization recipe could be applied to outpatient or lifetime records, but the paper's evaluation is limited to a single hospital system, so cross-hospital transferability is untested.
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 introduces EHR2Path, a multimodal LLM-based framework that serializes heterogeneous EHR data (ED, ward, ICU) into structured text, uses a Masked Summarization Bottleneck to compress long-term history into a small set of learned summary tokens, and is trained to predict the next hour's sparse EHR state. The authors propose three variants (text-only, summary-only, summary+text) and two fine-tuning strategies, and evaluate on nine tasks over MIMIC-IV, including next-time-step prediction, rolling simulation up to 24 hours or 3 days, and outcome prediction. The main reported results are next-hour event macro/micro F1 values around 0.47/0.78 for the text model and 0.48/0.76 for the summary model, vastly above a statistical baseline (0.02), with competitive or better results than the ETHOS baseline on shared tasks, and improvements after fine-tuning for outcome prediction.

Significance. If the claims are substantiated, EHR2Path is a valuable contribution: it demonstrates that a single LLM can ingest a broad set of routine EHR modalities (including free-text notes and dense ICU chart events) and produce reasonable next-hour forecasts, and the Masked Summarization Bottleneck is a practical technique for extending effective context length without proportional token growth (reportedly up to 20x more historical context at similar input size). The evaluation is patient-level split, uses held-out test sets, and reports confidence intervals; the code is released. However, the paper's headline claim of "iterative simulation of complete in-hospital trajectories" is not actually evaluated, and the lossy hourly aggregation may obscure clinically important sub-hourly dynamics. These issues are load-bearing for the clinical-simulation narrative, though the underlying forecasting and summarization machinery appear sound and the claims could be re-scoped or the evaluation extended.

major comments (3)
  1. [Abstract; Section 4.2; Appendix Table 7] The paper repeatedly claims that EHR2Path enables "iterative simulation of complete in-hospital trajectories," but no experiment in Section 4.2 or Appendix Table 7 rolls out a simulation from admission to discharge or death. Every rolling simulation task is capped at "24h or until unit discharge" (ED Vital Signs, Hospital Medications, Hospital Lab Values, ICU Vital Signs, ICU Inputs), ICU Imminent Mortality is capped at 24h, and ICU Imminent Discharge at 3 days. Hospital Discharge Diagnosis is a direct, non-rolling prediction with the LOS indicator set to zero. The LOS indicator is designed to let rollouts terminate, but Table 4 only measures its effect on ICU Imminent Discharge (a 3-day horizon) and on a direct diagnosis task, not on a full-stay rollout. The central differentiating claim is therefore unsupported by the reported evidence.
  2. [Section 5.2; Figure 5] The simulation evaluation does not go beyond a 24-hour horizon in Figure 5, yet the paper claims "extended time horizons" and "full in-hospital pathways," while hospital stays in MIMIC-IV often span hundreds of hours. Because iterative autoregressive rollout accumulates errors, the observed gradual decay over 24 hours does not establish stability over multi-day stays. The paper should either include full-stay rollouts (or at least multi-day rollouts with termination) or explicitly re-scope the claims to short-horizon simulation.
  3. [Section 4.1] The preprocessing aggregates all raw EHR events hourly (average for numerical values, most frequent for categoricals) before serialization into text. This step discards sub-hourly dynamics, including rapid vital-sign swings and acute deterioration that are clinically important in ICU settings. As a consequence, the predicted trajectories may be smooth but clinically misleading, and the paper's claim of preserving "real-world noise and incompleteness" is undercut. The authors should provide evidence that hourly aggregation preserves acute changes (e.g., by comparing against raw event-level evaluation for high-frequency chart events) or explicitly state this limitation in Section 5.5.
minor comments (4)
  1. [Section 3.2.3] The phrase "noise (+-20%)" should be written as "noise (±20%)" or "noise in the range ±20%"; as written it is ambiguous whether noise is added multiplicatively or additively.
  2. [Section 5.2 / Table 2] The sentence "E2P-S+T consistently ranks first or second" is not fully supported by Table 2: for ICU Imminent Mortality, E2P-S+T (0.57) is below ETHOS (0.61) and E2P-T (0.53) is below, while E2P-S is 0.50; the consistency claim should be qualified with the confidence intervals or the specific tasks where it holds.
  3. [Section 5.4 / Table 5] Table 5 selects bottleneck size 8 based only on validation loss after a limited number of training steps; no downstream task metric (e.g., next-step F1 or simulation accuracy) is reported across sizes, so it is unclear whether the chosen size is optimal for the actual prediction tasks.
  4. [Section 5.1] The phrase "strong baselines" in the abstract overstates the comparison: the statistical baseline is intentionally trivial, and ETHOS is not adapted to the full feature space or the exact output format of EHR2Path; the restricted-data comparison gives a more honest picture, though ETHOS's lower F1 there (0.04 vs 0.12) still favors the proposed model.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: predictions are evaluated on a patient-disjoint held-out test set, and no fitted parameter or self-citation reduces the central claim to its inputs.

full rationale

The paper's derivation chain is an empirical supervised-learning pipeline, and I found no step where a prediction is defined in terms of the target or where a fitted parameter is renamed as a prediction. Section 4.1 states that "Training samples consist of EHR data D_p,t up to time t, with t+1 as the label," and evaluation is performed on a patient-level disjoint test set, so next-hour forecasting and rolling simulation are measured against held-out future states, not re-fit to the test labels. The Masked Summarization Bottleneck is a learned representation trained to predict the next state, but this is a standard train/evaluation setup; Table 1 and Table 2 report held-out generalization, not a by-construction identity. The Length-of-Stay indicator is an auxiliary countdown mechanism, and the paper explicitly guards against label leakage: "at inference time, we never include the ground truth LOS token in the input, instead the first step is prompted without LOS token, while in later steps the predicted LOS tokens of prior steps are included" (Section 3.2.3). Thus the improved convergence reported in Table 4 is not achieved by feeding the true discharge time into the model at test time. The only self-citation (Pellegrini et al., 2023) appears in the related-work survey as an example of methods using additional numerical/categorical data; it is not load-bearing for any architectural choice, baseline, or evaluation. The abstract's phrase "iterative simulation of complete in-hospital trajectories" is broader than the 24-hour or 3-day output windows specified in Appendix Table 7, but that is an evaluation-scope gap, not a circular reduction: the rolling simulations are still genuinely autoregressive and scored against future ground truth. No equation in the paper reduces a predicted quantity to an input feature by construction, and no uniqueness claim is imported from the authors' prior work. I therefore find no significant circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on domain assumptions about the EHR data representation, not on mathematical axioms. There are no invented entities; the summary tokens are learned parameters. The most load-bearing choices are hourly aggregation, text serialization, the next-hour autoregressive target, and the bottleneck size. These are engineering assumptions, listed for completeness.

free parameters (3)
  • Number of summary tokens per section (m) = 8
    Selected via validation loss vs. token cost (Table 5); the claimed 625x compression is defined relative to this value.
  • Recent text window (w) = 24 hours
    Default context window for E2P-T and E2P-S+T; results vary for E2P-T-1h, so this is a hand-picked hyperparameter.
  • LOS indicator noise magnitude = ±20%
    Noise added to the ground-truth length-of-stay countdown during training to force re-estimation; not swept.
assumptions (5)
  • domain assumption Hourly aggregation preserves clinically relevant signal
    Section 4.1: raw vitals/labs are aggregated hourly using mean/mode. If sub-hourly dynamics matter, next-hour predictions are built on lossy inputs.
  • domain assumption Text serialization is a sufficient representation for an LLM
    Section 3.1: all structured data are converted to natural-language text; the paper assumes Qwen-0.5B can reason over this representation without bespoke encodings.
  • domain assumption Next-hour sparse state prediction is a valid pathway-modeling objective
    Section 3.2.1: the model is trained to predict all actively recorded features at t+1. This assumes next-hour event prediction is a faithful proxy for clinically meaningful trajectory simulation.
  • domain assumption Information bottleneck framing is appropriate
    Section 3.2.2 cites Tishby et al. and uses this to justify summary tokens; no derivation that the implementation realizes this objective.
  • domain assumption MIMIC-IV is representative of routine EHRs
    The authors note the limitation of a single healthcare system in Section 5.5; generalizability to other hospitals is assumed, not demonstrated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records." pith.science (2026). https://pith.science/paper/MVWKK6BQ

@misc{pith2026250604831,
  author       = {Pith},
  title        = {Pith review of: EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MVWKK6BQ}},
  note         = {Machine review of arXiv:2506.04831}
}
read the original abstract

Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data. Yet, existing approaches typically focus on isolated prediction tasks, narrow feature spaces, or short context windows, limiting their ability to model full patient pathways. To address this gap, we introduce EHR2Path, a multimodal framework for forecasting and simulating full in-hospital patient pathways from routine EHRs. EHR2Path converts diverse clinical inputs into a unified temporal representation, enabling modeling of a substantially broader set of patient information, including radiology reports, physician notes, vital signs, medication and laboratory patterns, and dense bedside charting. To support long clinical histories and broad feature spaces, we introduce a Masked Summarization Bottleneck that compresses long-term history into compact, task-optimized summary tokens while preserving recent context, improving both performance and token efficiency. In retrospective experiments on MIMIC-IV, EHR2Path enables next-step pathway forecasting and iterative simulation of complete in-hospital trajectories, while outperforming strong baselines on directly comparable tasks. These results demonstrate the feasibility of pathway-level modeling from routine EHRs, and indicate potential for supporting anticipatory clinical decision-making. Our code is available at https://github.com/ChantalMP/EHR2Path.

Figures

Figures reproduced from arXiv: 2506.04831 by the authors.

Figure 1
Figure 1. Short-term Patient Pathway visualization within Emergency Department, Hospital and ICU. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of our proposed method. A patient record is structured into text from which a fixed time [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Masked Summarization Bottleneck. Input tokens encode past observations, while summary tokens [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Overview of the evaluation across simulation and outcome tasks, summarizing task coverage and [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Development of event detection (F1) and value prediction (MAE) across simulation horizons for [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Qualitative Example of the ICU Vital Sign Simulation over 24 hours, showing ground-truth (blue), [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]
Figure 7
Figure 7. Figure 7: Example of structured input data from a patient record. [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: Examples of an expected output for the next hour during the stay (top) and at the end of the stay [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]

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

  1. A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models

    cs.LG 2025-07 reject novelty 4.0 of 10

    A survey that taxonomizes EHR modeling research into data-centric, architectural, learning-focused, multimodal, and LLM-based categories, with datasets and metrics.

Reference graph

Works this paper leans on

37 extracted references · 24 canonical work pages · cited by 1 Pith paper

  1. [1]

    Unsloth, 2023

    Michael Han Daniel Han and Unsloth team. Unsloth, 2023. URL http://github.com/unslothai/unsloth

    work page 2023

  2. [2]

    Snomed ct standard ontology based on the ontology for general medical science

    Shaker El-Sappagh, Francesco Franda, Farman Ali, and Kyung-Sup Kwak. Snomed ct standard ontology based on the ontology for general medical science. BMC medical informatics and decision making, 18: 0 1--19, 2018

    work page 2018

  3. [3]

    Ehrmamba: Towards generalizable and scalable foundation models for electronic health records

    Adibvafa Fallahpour, Mahshid Alinoori, Arash Afkanpour, and Amrit Krishnan. Ehrmamba: Towards generalizable and scalable foundation models for electronic health records. arXiv preprint arXiv:2405.14567, 2024

    arXiv 2024

  4. [4]

    Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals

    Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. circulation, 101 0 (23): 0 e215--e220, 2000

    2000

  5. [5]

    Lo RA : Low-rank adaptation of large language models

    Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lo RA : Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022

    2022

  6. [6]

    Unihpf: Universal healthcare predictive framework with zero domain knowledge

    Kyunghoon Hur, Jungwoo Oh, Junu Kim, Jiyoun Kim, Min Jae Lee, Eunbyeol Cho, Seong-Eun Moon, Young-Hak Kim, and Edward Choi. Unihpf: Universal healthcare predictive framework with zero domain knowledge. arXiv preprint arXiv:2211.08082, 2022

    arXiv 2022

  7. [7]

    Genhpf: General healthcare predictive framework for multi-task multi-source learning

    Kyunghoon Hur, Jungwoo Oh, Junu Kim, Jiyoun Kim, Min Jae Lee, Eunbyeol Cho, Seong-Eun Moon, Young-Hak Kim, Louis Atallah, and Edward Choi. Genhpf: General healthcare predictive framework for multi-task multi-source learning. IEEE Journal of Biomedical and Health Informatics, 28 0 (1): 0 502--513, 2023

    work page 2023

  8. [8]

    Mimic-iv (version 2.2)

    A Johnson, L Bulgarelli, T Pollard, S Horng, LA Celi, and R Mark IV. Mimic-iv (version 2.2). physionet.[internet], 2023 a

    work page 2023

Show all 37 references
  1. [9]

    Mimic-iv-ed

    Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Leo Anthony Celi, Roger Mark, and S Horng IV. Mimic-iv-ed. PhysioNet, 2021

    2021

  2. [10]

    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 b

    2023

  3. [11]

    General-purpose retrieval-enhanced medical prediction model using near-infinite history

    Junu Kim, Chaeeun Shim, Bosco Seong Kyu Yang, Chami Im, Sung Yoon Lim, Han-Gil Jeong, and Edward Choi. General-purpose retrieval-enhanced medical prediction model using near-infinite history. In Proceedings of the 9th Machine Learning for Healthcare Conference, volume 252 of P...

    2024

  4. [12]

    Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study

    Zeljko Kraljevic, Dan Bean, Anthony Shek, Rebecca Bendayan, Harry Hemingway, Joshua Au Yeung, Alexander Deng, Alfred Baston, Jack Ross, Esther Idowu, et al. Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retr...

    2024

  5. [13]

    Emergency department decision support using clinical pseudo-notes

    Simon A Lee, Sujay Jain, Alex Chen, Kyoka Ono, Jennifer Fang, Akos Rudas, and Jeffrey N Chiang. Emergency department decision support using clinical pseudo-notes. arXiv preprint arXiv:2402.00160, 2024

    2024 arXiv

  6. [14]

    Behrt: transformer for electronic health records

    Yikuan Li, Shishir Rao, Jos \'e Roberto Ayala Solares, Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Yajie Zhu, Kazem Rahimi, and Gholamreza Salimi-Khorshidi. Behrt: transformer for electronic health records. Scientific reports, 10 0 (1): 0 1--12, 2020

    2020

  7. [15]

    Hi-behrt: hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records

    Yikuan Li, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Shishir Rao, Abdelaali Hassaine, Dexter Canoy, Thomas Lukasiewicz, and Kazem Rahimi. Hi-behrt: hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic hea...

    2022

  8. [16]

    Revisiting the mimic-iv benchmark: Experiments using language models for electronic health records

    Jes \'u s Lov \'o n-Melgarejo, Thouria Ben-Haddi, Jules Di Scala, Jos \'e G Moreno, and Lynda Tamine. Revisiting the mimic-iv benchmark: Experiments using language models for electronic health records. In Proceedings of the First Workshop on Patient-Oriented Language Processin...

    2024

  9. [17]

    Large language models forecast patient health trajectories enabling digital twins

    Nikita Makarov, Maria Bordukova, Papichaya Quengdaeng, Daniel Garger, Raul Rodriguez-Esteban, Fabian Schmich, and Michael P Menden. Large language models forecast patient health trajectories enabling digital twins. npj Digital Medicine, 8 0 (1): 0 588, 2025

    2025

  10. [18]

    A comprehensive ehr timeseries pre-training benchmark

    Matthew McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, and Marzyeh Ghassemi. A comprehensive ehr timeseries pre-training benchmark. In Proceedings of the Conference on Health, Inference, and Learning, pp.\ 257--278, 2021

    2021

  11. [19]

    Event stream gpt: a data pre-processing and modeling library for generative, pre-trained transformers over continuous-time sequences of complex events

    Matthew McDermott, Bret Nestor, Peniel Argaw, and Isaac S Kohane. Event stream gpt: a data pre-processing and modeling library for generative, pre-trained transformers over continuous-time sequences of complex events. Advances in Neural Information Processing Systems, 36: 0 24...

    2023

  12. [20]

    Cehr-bert: Incorporating temporal information from structured ehr data to improve prediction tasks

    Chao Pang, Xinzhuo Jiang, Krishna S Kalluri, Matthew Spotnitz, RuiJun Chen, Adler Perotte, and Karthik Natarajan. Cehr-bert: Incorporating temporal information from structured ehr data to improve prediction tasks. In Machine Learning for Health, pp.\ 239--260. PMLR, 2021

    2021

  13. [21]

    Cehr-gpt: Generating electronic health records with chronological patient timelines

    Chao Pang, Xinzhuo Jiang, Nishanth Parameshwar Pavinkurve, Krishna S Kalluri, Elise L Minto, Jason Patterson, Linying Zhang, George Hripcsak, Gamze G \"u rsoy, No \'e mie Elhadad, et al. Cehr-gpt: Generating electronic health records with chronological patient timelines. arXiv...

    2024 arXiv

  14. [22]

    Unsupervised pre-training of graph transformers on patient population graphs

    Chantal Pellegrini, Nassir Navab, and Anees Kazi. Unsupervised pre-training of graph transformers on patient population graphs. Medical Image Analysis, 89: 0 102895, 2023

    2023

  15. [23]

    Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction

    Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi. Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ digital medicine, 4 0 (1): 0 86, 2021

    2021

  16. [24]

    Zero shot health trajectory prediction using transformer

    Pawel Renc, Yugang Jia, Anthony E Samir, Jaroslaw Was, Quanzheng Li, David W Bates, and Arkadiusz Sitek. Zero shot health trajectory prediction using transformer. NPJ Digital Medicine, 7 0 (1): 0 256, 2024

    2024

  17. [25]

    Understanding patient pathways in the context of integrated health care services-implications from a scoping review

    Peggy Richter and Hannes Schlieter. Understanding patient pathways in the context of integrated health care services-implications from a scoping review. Internationalen Tagung Wirtschaftsinformatik, 2019

    2019

  18. [26]

    The international classification of diseases: ninth revision (icd-9), 1978

    Vergil N Slee. The international classification of diseases: ninth revision (icd-9), 1978

    1978

  19. [27]

    Motor: A time-to-event foundation model for structured medical records

    Ethan Steinberg, Jason Fries, Yizhe Xu, and Nigam Shah. Motor: A time-to-event foundation model for structured medical records. arXiv preprint arXiv:2301.03150, 2023

    2023 arXiv

  20. [28]

    Pereira, and William Bialek

    Naftali Tishby, Fernando C. Pereira, and William Bialek. The information bottleneck method. In Proc. of the 37-th Annual Allerton Conference on Communication, Control and Computing, pp.\ 368--377, 1999

    1999

  21. [29]

    Attention is all you need

    A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017

    2017

  22. [30]

    Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii

    Shirly Wang, Matthew BA McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C Hughes, and Tristan Naumann. Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii. In Proceedings of the ACM conference on health, inference, and learning, pp...

    2020

  23. [31]

    Ehrshot: An ehr benchmark for few-shot evaluation of foundation models

    Michael Wornow, Rahul Thapa, Ethan Steinberg, Jason Fries, and Nigam Shah. Ehrshot: An ehr benchmark for few-shot evaluation of foundation models. Advances in Neural Information Processing Systems, 36: 0 67125--67137, 2023

    2023

  24. [32]

    Context clues: Evaluating long context models for clinical prediction tasks on ehrs

    Michael Wornow, Suhana Bedi, Miguel Angel Fuentes Hernandez, Ethan Steinberg, Jason Alan Fries, Christopher R \'e , Sanmi Koyejo, and Nigam H Shah. Context clues: Evaluating long context models for clinical prediction tasks on ehrs. arXiv preprint arXiv:2412.16178, 2024

    2024 arXiv

  25. [33]

    Qwen2 technical report

    An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, et al. Qwen2 technical report. arXiv preprint arXiv:2407.10671, 2024

    2024 arXiv

  26. [34]

    A large language model for electronic health records

    Xi Yang, Aokun Chen, Nima PourNejatian, Hoo Chang Shin, Kaleb E Smith, Christopher Parisien, Colin Compas, Cheryl Martin, Anthony B Costa, Mona G Flores, et al. A large language model for electronic health records. NPJ digital medicine, 5 0 (1): 0 194, 2022

    2022

  27. [35]

    Transformehr: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records

    Zhichao Yang, Avijit Mitra, Weisong Liu, Dan Berlowitz, and Hong Yu. Transformehr: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records. Nature communications, 14 0 (1): 0 7857, 2023

    2023

  28. [36]

    Emerge: Integrating rag for improved multimodal ehr predictive modeling

    Yinghao Zhu, Changyu Ren, Zixiang Wang, Xiaochen Zheng, Shiyun Xie, Junlan Feng, Xi Zhu, Zhoujun Li, Liantao Ma, and Chengwei Pan. Emerge: Integrating rag for improved multimodal ehr predictive modeling. arXiv preprint arXiv:2406.00036, 2024

    2024 arXiv

  29. [37]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

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