{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T06:17:52Z","timestamp":1773296272310,"version":"3.50.1"},"reference-count":92,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100016017","name":"Duke-NUS Medical School","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016017","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,18]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structured data from January 2000 to October 2022 were included.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We identified 6077 articles and included 79 for the final analysis. Data-driven population segmentation analysis was employed in various clinical settings. K-means clustering is the most prevalent unsupervised ML paradigm. The most common settings were healthcare institutions. The most common targeted population was the general population.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Although all the studies did internal validation, only 11 papers (13.9%) did external validation, and 23 papers (29.1%) conducted methods comparison. The existing papers discussed little validating the robustness of ML modeling.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Existing ML applications on population segmentation need more evaluations regarding giving tailored, efficient integrated healthcare solutions compared to traditional segmentation analysis. Future ML applications in the field should emphasize methods\u2019 comparisons and external validation and investigate approaches to evaluate individual consistency using different methods.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocad111","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T20:34:25Z","timestamp":1687898065000},"page":"1573-1582","source":"Crossref","is-referenced-by-count":11,"title":["A scoping review of the clinical application of machine learning in data-driven population segmentation analysis"],"prefix":"10.1093","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7620-9076","authenticated-orcid":false,"given":"Pinyan","family":"Liu","sequence":"first","affiliation":[{"name":"Centre for Quantitative Medicine, Duke-NUS Medical School , Singapore, Singapore"}]},{"given":"Ziwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Centre for Quantitative Medicine, Duke-NUS Medical School , Singapore, Singapore"}]},{"given":"Nan","family":"Liu","sequence":"additional","affiliation":[{"name":"Centre for Quantitative Medicine, Duke-NUS Medical School , Singapore, Singapore"},{"name":"Programme in Health Services and Systems Research, Duke-NUS Medical School , Singapore, Singapore"},{"name":"Institute of Data Science, National University of Singapore , Singapore, Singapore"}]},{"given":"Marco Aur\u00e9lio","family":"Peres","sequence":"additional","affiliation":[{"name":"Programme in Health Services and Systems Research, Duke-NUS Medical School , Singapore, Singapore"},{"name":"National Dental Research Institute Singapore, National Dental Centre Singapore , Singapore, Singapore"}]}],"member":"286","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"issue":"3","key":"2023081808483773400_ocad111-B1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.7812\/TPP\/14-005","article-title":"Improving care for older adults: a model to segment the senior 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