ArticleJournal of the American Medical Informatics Association : JAMIA2025
Recovering missing electronic health record mortality data with a machine learning-enhanced data linkage process.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Index-window C-reactive protein and carcinoembryonic antigen and five-year all-cause mortality in Hispanic/Latino and non-Hispanic White patients with colorectal cancer: a retrospective biomarker cohort study.Future science OA · 2026Article
- Extending the Observational Medical Outcomes Partnership Common Data Model to Support Observational Peripheral Vascular Disease Research.The Journal of surgical research · 2026Observational
- The Roles of Biomedical Informatics for an Equitable Learning Health System: Lessons from a Year of Scientific Informatics Literature.Methods of information in medicine · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveTo develop a continual process for linking more comprehensive external mortality data to electronic health records (EHRs) for a large healthcare system, which can serve as a template for other healthcare systems. MATERIALS AND
methodsMonthly updates of state death records were arranged, and an automated pipeline was developed to identify matches with patients in the EHR. A machine learning classifier was used to closely match human classification performance of potential record matches.
resultsThe automated linkage process achieved high performance in classifying potential record matches, with a sensitivity of 99.3% and specificity of 98.8% relative to manual classification. Only 22.4% of identified patient deaths were previously indicated in the EHR. DISCUSSION AND
conclusionsWe 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.
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