Work

A Comparative Analysis of Patient Similarity Measures for Outcome Prediction.

Year: 2025

Type: article

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.

Source: PubMed

Authors: Deyi Li, Alan S L Yu, Mei Liu

Institutions: University of Florida Health, University of Florida, University of Kansas Medical Center

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases

Awards: Identifying Personalized Risk of Acute Kidney Injury with Machine Learning, Personalized Machine Learning for Acute Kidney Injury Prediction and Prognosis