ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025
A Comparative Analysis of Patient Similarity Measures for Outcome Prediction.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Personalized medicine aims to improve clinical outcomes by tailoring treatments to individual patients based on genetic, phenotypic, or psychosocial characteristics, leveraging insights from similar patients. This is particularly necessary for managing diseases with significant variability in their causes, progressions and prognoses. Accurate measurement of patient similarity is crucial in this context, as it enables the identification of a high-quality cohort of similar patients, thereby enhancing clinical decision making with better evidence. However, previous studies have not comprehensively compared different patient similarity measures in large-scale retrospective analyses of electronic health records (EHRs). In this study, we conducted a comparative analysis of four patient similarity measures focusing on feature weighting mechanisms, using EHR data from 46,968 hospitalized patients. For evaluation, we assessed the patient similarity measures for predicting acute kidney injury, readmission, and mortality. Our results showed that using grid-searched weights to combine features based by their types outperformed all other methods.
Identifiers
40502217PMC12150746What OpenQuestion holds
Registered trials
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