Evidence map›Paper›PMID 42317857›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2026

Large Models for Small Tables: Adapting Tabular Foundation Models to EHR Data.

Rui Zhu, Xiaopu Zhou, Ivy Liang, Stephen W Scherer, Ke Xu, Lucila Ohno-Machado

Abstract read
PubMed Full text at PMC
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Foundation Model-Guided Synthetic EHR Release: Performance Enhancement with Privacy Preservation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Rui ZhuYale University, New Haven, CT, USA.
Xiaopu ZhouThe Hospital for Sick Children, Toronto, ON, Canada.
Ivy LiangYale University, New Haven, CT, USA.
Stephen W SchererThe Hospital for Sick Children, Toronto, ON, Canada.
Ke XuYale University, New Haven, CT, USA.
Lucila Ohno-MachadoYale University, New Haven, CT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic Health Records (EHR) contain abundant structured tabular data, yet developing accurate predictive models on small clinical datasets remains challenging. Foundation models have transformed natural language and imaging tasks by leveraging pretraining on large-scale data, and they are increasingly explored in healthcare as a route toward generalist medical AI. However, this paradigm remains largely unexplored for structured EHR data. In this study, we explore the adaptation of a tabular foundation model to EHR predictive tasks. We fine-tune

Identifiers

PMID42317857
PMCPMC13274287

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.