Mitigation of outcome conflation in predicting patient outcomes using electronic health records

The number of predicted outcomes needs to be carefully considered when employing AI disease risk prediction models.

Authors

Nima Aghaeepour (ORCID: https://orcid.org/0000-0002-6117-8764)
Camilo Espinosa (ORCID: https://orcid.org/0000-0003-1630-1564)
Tomin James (ORCID: https://orcid.org/0000-0001-9010-7423)
S. Momsen Reincke (ORCID: https://orcid.org/0000-0002-8132-3527)
Eloïse Berson (ORCID: https://orcid.org/0000-0003-1046-125X)
Philip Chung (ORCID: https://orcid.org/0000-0002-1194-7510)

Institutions

Stanford University (US)

Publication Details

Journal: Journal of the American Medical Informatics Association
Published: 2025-03-08
DOI: https://doi.org/10.1093/jamia/ocaf033
Citations: 3
Primary Topic: Machine Learning in Healthcare
Type: article
Field-Weighted Citation Impact: 7.44

Funders

Burroughs Wellcome Fund
Bill and Melinda Gates Foundation
March of Dimes Foundation
Robertson Foundation
National Institutes of Health

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article

Mitigation of outcome conflation in predicting patient outcomes using electronic health records

Nima Aghaeepour, Camilo Espinosa, Tomin James, S. Momsen Reincke et al.

Mar 8, 2025

3 citations

Journal of the American Medical Informatics Association

Machine Learning in Healthcare

7.44

article

Mitigation of outcome conflation in predicting patient outcomes using electronic health records

Nima Aghaeepour, Camilo Espinosa, Tomin James, S. Momsen Reincke, Eloïse Berson, Philip Chung

March 8, 2025article en

3 citations

Abstract

The number of predicted outcomes needs to be carefully considered when employing AI disease risk prediction models.

Journal of the American Medical Informatics AssociationVol. 32(5)

Stanford University (US)

Burroughs Wellcome Fund, Bill and Melinda Gates Foundation, March of Dimes Foundation, Robertson Foundation, National Institutes of Health

Openalex Percentile: Top 4%

Machine Learning in Healthcare

7.44

DOI Open Access PDF OpenAlex

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Mitigation of outcome conflation in predicting patient outcomes using electronic health records — Nima Aghaeepour, Camilo Espinosa, et al. · Journal of the American Medical Informatics Association (2025) | TGRS Research Map | TGRS