EHR Foundation Models

EHR: Electronic Health Record

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23 papers5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

How Deep is the Signal? Time-Context up to 30 Years for Colorectal Cancer Prediction in a Primary-Care EHR Model
Nov 5, 2026Achille Raviez, Lan Wang, Brendan DelaneyLongitudinal EHR Modeling Clinical Prediction
I-X, Imperial College London, United Kingdom · Sorbonne Université, AP-HP, France
TRACER, a time-to-event EHR foundation model for disease detection across health systems
Oct 29, 2026Mara Pleasure, Ekaterina Redekop, Vedrana Ivezic +4EHR Foundation Models Longitudinal EHR Modeling
Biomedical AI Research Lab, University of California, Los Angeles, Los Angeles, CA, USA · Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA · Bioinformatics, University of California, Los Angeles, Los Angeles, CA, USA +4
A general-purpose System One decision model for clinical records
Oct 19, 2026Andrew Zhang, Trinh T. L. Vuong, Sophia J. Wagner +14EHR QA EHR Foundation Models
Mass General Brigham AI Institute, Boston, MA. · Harvard Medical School, Boston, MA. · Massachusetts Institute of Technology, Cambridge, MA.
Discrete Neural Interlingua for Ontology-Agnostic EHR Modeling
Oct 19, 2026Tong Ding, Caiwei Tian, Dongmin Bang +6Tokenization in Language Models EHR Foundation Models
Mass General Brigham AI Institute · Harvard University · Massachusetts Institute of Technology
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, 2026Xiang Gao, Haresh Rengaraj Rajamohan, 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

Nov 5, 2026J/K move · Enter open · S save

How Deep is the Signal? Time-Context up to 30 Years for Colorectal Cancer Prediction in a Primary-Care EHR Model

Achille Raviez, Lan Wang, Brendan Delaney

I-X, Imperial College London, United Kingdom · Sorbonne Université, AP-HP, France

Longitudinal EHR Modeling Clinical Prediction EHR Foundation Models Clinical Risk Prediction

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EHR foundation models read medical events within a fixed context window, and long context has so far been measured only in tokens. How far back in time a model must look to capture the earliest predictive signal is unknown. We introduce time-context, the calendar lookback window before the prediction point, and provide, to our knowledge, the first systematic evaluation of decade-scale time-context for an EHR foundation model. We pretrained a decoder-only transformer (34.6M parameters) on 1.4B coded events from 3.6M primary-care patients in North West London and fine-tuned it to predict incident colorectal cancer under a 12-month pre-diagnostic buffer. A frozen cohort of 1,008 cases and 9,582 matched controls, all registered for at least 30 years, was evaluated at 15 nested lookback windows from 2 to 30 years, so that every truncation is compared within the same patients. Discrimination rose from an AUROC of 0.551 at 2 years to 0.590 at 18 years, then plateaued, with about 80% of the gain reached within 10 years; an XGBoost baseline converged to the same level (peak AUROC 0.589). A simulation further suggests that rotary position embeddings (RoPE) over elapsed time alias beyond about 20 years at the default base frequency, motivating a retuned

θ

. Time-context is a measurable design axis for clinical prediction models, distinct from token-context, and should be reported.