EHR Foundation Models

EHR: Electronic Health Record

Follow this topic Ask about this topic

Latest papers

19 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

CODA: Cohort- and Drift-aware Foundation Model for Multimodal Clinical Reasoning
Oct 19, 2026Changshuo Liu, Jiaqi Zhu, Wenqiao Zhang +2EHR Foundation Models Multimodal Reasoning
School of Computing National University of Singapore · School of Software Technology Zhejiang University
Sequence Composition in EHR Foundation Model Pretraining
Oct 12, 2026Joshua Placidi, Inigo Stibbe, Alfred J. Balston +2EHR Foundation Models Electronic Health Records
Department of Computing, Imperial College London
EHRAdapt: Adapting Pretrained Language Models to Electronic Health Records with Semantic Priors for Rare Clinical Events
Sep 27, 2026Andre R Goncalves, Vincent Liu, Priyadip RayDomain Adaptation for LLMs Long-Tail Learning
Lawrence Livermore National Laboratory · Kaiser Permanente
Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models
Sep 10, 2026Yuxin Xiao, Sheng Zhang, Chandan Singh +4EHR Foundation Models Reinforcement Learning
MIT · Microsoft Research
Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records
Aug 20, 2026Jun Ni Du, Lukas Adamek, Maxim Kryukov +4EHR Foundation Models Clinical Outcome Prediction
Data & Computational Science, R&D, Sanofi Toronto, Canada · Data & Computational Science, R&D, Sanofi Barcelona, Spain · Clinical Real World Evidence, R&D, Sanofi Cambridge, USA +2
Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic
Aug 17, 2026Simon Ellershaw, Christopher Tomlinson, Zeljko Kraljevic +6EHR Foundation Models Next-Event Prediction
Institute of Health Informatics, University College London, London, UK · Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK · King’s Institute for Artificial Intelligence, King’s College London, London, UK +13
Federated generative event models for tokenized electronic health records
Aug 3, 2026Michael C. Burkhart, Luke Solo, Inhyeok Lee +8Clinical Outcome Prediction Generative Modeling
Department of Medicine, University of Chicago, Chicago, Illinois, USA · Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
Autoregressive EHR Foundation Models with Multimodal Inputs
Jul 24, 2026Yuxuan Liu, Joshua Placidi, Jinpei Han +3Clinical Outcome Prediction Multimodal Token Compression
Dept. of Computing, Imperial College London, London, United Kingdom · UKRI Centres in AI for Health, United Kingdom · School of Public Health, Imperial College London, London, United Kingdom +2
Pretraining EHR Foundation Models with Patient-Aware Sampling
Jul 24, 2026Joshua Placidi, Yuxuan Liu, Jinpei Han +2EHR Foundation Models Longitudinal EHR Modeling
Dept. of Computing, Imperial College London, London, United Kingdom · Dept. of Bioengineering, Imperial College London, London, United Kingdom · UKRI Centres in AI for Health, United Kingdom +1
TEDDY: A Pediatric Foundation Model for Risk Forewarning from ICD-Coded Diagnostic Histories
Jul 15, 2026Matthew Brady Neeley, Jorge Botas, Johnathan Jia +5EHR Foundation Models Clinical Prediction
Baylor College of Medicine
PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models
Jun 23, 2026Lin Lawrence Guo, Adam Paul Yan, Emily Vettese +1EHR Foundation Models Clinical Outcome Prediction
Child Health Evaluative Sciences · The Hospital for Sick Children · Toronto, Canada +1
Cohort-Anchored Foundation Models for Electronic Health Records: From Risk Scores to Auditable Peer Cohorts
Jun 20, 2026Kaiping ZhengClinical Decision-Making Multimodal Model Interpretability
National University of Singapore
ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
Jun 1, 2026Bo-Hong Wang, Baicheng Peng, Ruilin Wang +3LLM Grounding LLM Alignment
School of Computer Science, McGill University, Montreal, QC, Canada · Mila - Quebec AI Institute, Montreal, QC, Canada
DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System
May 14, 2026Yunying Zhu, Andrew R Weckstein, Kueiyu Joshua Lin +1Clinical Prediction Time Series Transformers
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA · Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA · Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA +2
Sparse Autoencoder Decomposition of Clinical Sequence Model Representations: Feature Complexity, Task Specialisation, and Mortality Prediction
Apr 13, 2026Chris Sainsbury, Feng Dong, Andreas KarwathEHR Foundation Models Sparse Autoencoders
School of Cardiovascular & Metabolic Health, University of Glasgow, UK · School of Medicine, University of Dundee, UK · NHS Greater Glasgow and Clyde, UK +2
Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints
Apr 2, 2026Minh-Khoi Pham, Thang-Long Nguyen Ho, Thao Thi Phuong Dao +8Tabular Foundation Models Retrieval-Augmented ICL
School of Computing, Dublin City University, Dublin, Ireland · ADAPT Centre, Dublin City University, Dublin, Ireland · University of Science, Vietnam National University, Ho Chi Minh City, Vietnam +5
Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction
Mar 25, 2026Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu +10EHR Foundation Models Longitudinal EHR Modeling
Center for Data Science, New York University, New York, NY, USA · Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA · Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, USA +1
EveryQuery: A Promptable Foundation Model for Clinical Prediction Tasks over Electronic Health Records
Mar 9, 2026Payal Chandak, Gregory Kondas, Liat Antwarg Friedman +2EHR Foundation Models Clinical Outcome Prediction
Harvard-MIT HST · Columbia University · Harvard Medical School
Tokenization Tradeoffs in Structured EHR Foundation Models
Mar 3, 2026Lin Lawrence Guo, Santiago Eduardo Arciniegas, Joseph Jihyung Lee +4EHR Foundation Models Longitudinal EHR Modeling
Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada · Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Canada · Department of Medicine, University Health Network, Toronto, Canada +1

Oct 19, 2026J/K move · Enter open · S save

CODA: Cohort- and Drift-aware Foundation Model for Multimodal Clinical Reasoning

Changshuo Liu, Jiaqi Zhu, Wenqiao Zhang, Xiaokui Xiao+1

School of Computing National University of Singapore · School of Software Technology Zhejiang University

EHR Foundation Models Multimodal Reasoning

Open paper AskPDF arXiv

Recent advances in multimodal foundation models have demonstrated strong performance across diverse clinical tasks. However, existing approaches predominantly operate at the individual sample level and fail to model the interplay between population-level cohort patterns and patient-specific variation that underlie real-world clinical reasoning. In practice, clinicians routinely reason through patient cohorts sharing similar demographic, physiological, or pathological characteristics, while accounting for patient-specific deviations from the group prototype. Motivated by this gap, we propose CODA, a cohort- and drift-aware foundation model for multimodal clinical reasoning. CODA explicitly integrates cohort structure into the modeling pipeline through two complementary mechanisms: cohort-aware population stratification and drift-aware individual adaptation. In particular, we leverage reinforcement learning to discover clinically meaningful patient cohorts, and further model each patient's deviation from cohort prototypes as a structured drift signal encoding fine-grained individual heterogeneity. These cohort- and drift-aware representations are incorporated into the foundation model to enable reasoning that is simultaneously informed by population-level patterns and sensitive to individual-level variation. Extensive experiments on real-world electronic health record (EHR) benchmarks demonstrate that CODA achieves consistent effectiveness and strong generalizability across diverse clinical tasks, including closed-QA, open-QA, and report generation.