Leveraging electronic health records for atrial fibrillation cohort generation

(1) Discharge reports are a valuable resource for automatic cohort selection, with both the rule-based method and LLMs showing promising results. (2) While LLMs struggled with long-context inputs, they handled temporal reasoning well when explicit dates were provided. (3) Larger models did not always outperform smaller ones, (4) prompt language strongly influenced performance, and medical model variants were not consistently superior.

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Publication Details

Journal
Health Information Science and Systems
Published
2026-01-07
DOI
https://doi.org/10.1007/s13755-025-00415-w
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

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article

Leveraging electronic health records for atrial fibrillation cohort generation

Aitziber Atutxa, Ane García Domingo-Aldama, Marcos Merino Prado, Josu Goikoetxea et al.
Health Information Science and Systems
Machine Learning in Healthcare
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Leveraging electronic health records for atrial fibrillation cohort generation

Aitziber Atutxa, Ane García Domingo-Aldama, Marcos Merino Prado, Josu Goikoetxea, Alain García-Olea, Koldo Gojenola
article en

Abstract

(1) Discharge reports are a valuable resource for automatic cohort selection, with both the rule-based method and LLMs showing promising results. (2) While LLMs struggled with long-context inputs, they handled temporal reasoning well when explicit dates were provided. (3) Larger models did not always outperform smaller ones, (4) prompt language strongly influenced performance, and medical model variants were not consistently superior.

Health Information Science and SystemsVol. 14(1)
University of the Basque Country (ES), Hospital de Basurto (ES)
Eusko Jaurlaritza, Agencia Estatal de Investigación
Quality Education
Openalex Percentile: Top 95%
Machine Learning in Healthcare
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