PORTER is a language-grounded EHR foundation model that uses text descriptions for events and a numeric pathway, matching fixed-vocabulary performance on 74 tasks while recovering 97.1% AUROC on unseen vocabularies and outperforming on MIMIC.
Med-BERT: pretrained contex- tualized embeddings on large-scale structured electronic health records for disease prediction
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PerCaM-Health learns evolving personalized dynamic causal graphs from longitudinal health data to enable more reliable patient-level counterfactual queries than cohort or per-patient baselines.
Sequence embeddings from diagnosis histories improve prediction of 93 of 131 incident disease blocks and event-free survival beyond age, sex, and comorbidity burden in large-scale hospital data.
Disease trajectory embeddings from longitudinal EHR data serve as structural priors to enhance multi-organ IDP representation learning, improving AUC and MAE for disease prediction across 159 conditions in UK Biobank.
Fused code-value tokenization improves mortality AUROC from 0.891 to 0.915 and other clinical outcome predictions, while certain temporal encodings like event order match or exceed time tokens with shorter sequences.
SurvBench supplies a configurable, open-source preprocessing pipeline that standardizes multi-modal EHR data from four critical-care databases for single-risk and competing-risk survival analysis.
DT-Transformer predicts next disease events with median age- and sex-stratified AUC 0.871 across 896 categories on held-out and prospective data from a 1.7M-patient multi-hospital EHR dataset.
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PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning
PerCaM-Health learns evolving personalized dynamic causal graphs from longitudinal health data to enable more reliable patient-level counterfactual queries than cohort or per-patient baselines.