{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T20:33:20Z","timestamp":1776198800692,"version":"3.50.1"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"British Heart Foundation Data Science Centre","award":["SP\/19\/3\/34678"],"award-info":[{"award-number":["SP\/19\/3\/34678"]}]},{"name":"British Heart Foundation Data Science Centre","award":["R01 GM139891"],"award-info":[{"award-number":["R01 GM139891"]}]},{"name":"British Heart Foundation Data Science Centre","award":["R01AG069900"],"award-info":[{"award-number":["R01AG069900"]}]},{"name":"British Heart Foundation Data Science Centre","award":["U01 HG011181"],"award-info":[{"award-number":["U01 HG011181"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Introduction<\/jats:title>\n                  <jats:p>Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Recommendations<\/jats:title>\n                  <jats:p>Here, we propose five independent fundamental dimensions of phenotyping algorithms\u2014complexity, performance, efficiency, implementability, and maintenance\u2014through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae005","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T09:50:05Z","timestamp":1706176205000},"page":"1036-1041","source":"Crossref","is-referenced-by-count":7,"title":["Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions"],"prefix":"10.1093","volume":"31","author":[{"given":"Wei-Qi","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN 37203, United States"}]},{"given":"Robb","family":"Rowley","sequence":"additional","affiliation":[{"name":"National Human Genome Research Institute , Bethesda, MD 20892, United 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