Recovering missing electronic health record mortality data with a machine learning-enhanced data linkage process

We developed a solution for recovering missing mortality data for EHR that is effective, scalable for cost and computation, and sustainable over time. These recovered mortality data now supplement the EHR data available for research purposes.

Authors

Peter Leese (ORCID: https://orcid.org/0000-0001-9730-1675)
Sofia Dard (ORCID: https://orcid.org/0000-0002-0286-2515)
Samyuktha Nandhakumar
John Powers (ORCID: https://orcid.org/0000-0002-1471-6259)
Paul I Kovach (ORCID: https://orcid.org/0009-0007-1562-1095)

Institutions

University of North Carolina at Chapel Hill (US)

Publication Details

Journal: Journal of the American Medical Informatics Association
Published: 2025-04-15
DOI: https://doi.org/10.1093/jamia/ocaf060
Citations: 1
Primary Topic: Machine Learning in Healthcare
Type: article
Field-Weighted Citation Impact: 2.48

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article

Recovering missing electronic health record mortality data with a machine learning-enhanced data linkage process

Peter Leese, Sofia Dard, Samyuktha Nandhakumar, John Powers et al.

Apr 15, 2025

1 citations

Journal of the American Medical Informatics Association

Machine Learning in Healthcare

2.48

article

Recovering missing electronic health record mortality data with a machine learning-enhanced data linkage process

Peter Leese, Sofia Dard, Samyuktha Nandhakumar, John Powers, Paul I Kovach

April 15, 2025article en

1 citations

Abstract

We developed a solution for recovering missing mortality data for EHR that is effective, scalable for cost and computation, and sustainable over time. These recovered mortality data now supplement the EHR data available for research purposes.

Journal of the American Medical Informatics AssociationVol. 32(6)

University of North Carolina at Chapel Hill (US)

Openalex Percentile: Top 11%

Machine Learning in Healthcare

2.48

DOI Source OpenAlex

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