ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2026
Foundation Model-Guided Synthetic EHR Release: Performance Enhancement with Privacy Preservation.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Large Models for Small Tables: Adapting Tabular Foundation Models to EHR Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning on electronic health records (EHR) is constrained by limited data, strict privacy regulations, and distribution shifts across sites and time. Synthetic data offer a potential solution, but existing tabular generators often struggle in the small sample size in EHR regime. We propose MEDPFN, a distribution-aware synthetic EHR generator built on top of the TabPFN tabular foundation model. MEDPFN uses TabPFN as a learned compatibility score over EHR and adds a Mahalanobis regularizer that anchors generated samples to realistic feature statistics, stabilizing generation from small reference cohorts. On six public tabular EHR datasets, we treat the original data only as private context, train standard classifiers solely on MEDPFN-generated samples, and evaluate on held-out real patients. Across datasets and models, MEDPFN-based training matches or exceeds real-data baselines and outperforms several state-of-the-art synthetic generators, enabling high-utility, privacy-friendly EHR data release.
Identifiers
42317844PMC13274374What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.