{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T17:37:58Z","timestamp":1763141878666,"version":"3.37.3"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T00:00:00Z","timestamp":1662681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Health Data Research UK"},{"name":"UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care"},{"name":"Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division"},{"DOI":"10.13039\/501100001626","name":"Public Health Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001626","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","award":["RP-PG-0407-10314"],"award-info":[{"award-number":["RP-PG-0407-10314"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["086091\/Z\/08\/Z"],"award-info":[{"award-number":["086091\/Z\/08\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006662","name":"NIHR","doi-asserted-by":"publisher","award":["AI_AWARD01864","COV-LT-0009"],"award-info":[{"award-number":["AI_AWARD01864","COV-LT-0009"]}],"id":[{"id":"10.13039\/100006662","id-type":"DOI","asserted-by":"publisher"}]},{"name":"British Heart Foundation Accelerator Award","award":["AA\/18\/6\/24223"],"award-info":[{"award-number":["AA\/18\/6\/24223"]}]},{"name":"NIHR Strategic Priorities Award in Multimorbidity Research","award":["MR\/V033867\/1"],"award-info":[{"award-number":["MR\/V033867\/1"]}]},{"name":"Multimorbidity Mechanism and Therapeutics Research Collaborative"},{"name":"Health Data Research UK London"},{"DOI":"10.13039\/501100000276","name":"Department of Health and Social Care","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000276","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division"},{"DOI":"10.13039\/100012338","name":"The Alan Turing Institute","doi-asserted-by":"publisher","award":["EP\/N510129\/1"],"award-info":[{"award-number":["EP\/N510129\/1"]}],"id":[{"id":"10.13039\/100012338","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The British Heart Foundation Accelerator Award","award":["AA\/18\/6\/24223"],"award-info":[{"award-number":["AA\/18\/6\/24223"]}]},{"name":"The British Heart Foundation Data Science Centre"},{"name":"NIHR funded Multimorbidity Mechanism and Therapeutics Research Collaborative","award":["MR\/V033867\/1"],"award-info":[{"award-number":["MR\/V033867\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Patient phenotype definitions based on terminologies are required for the computational use of electronic health records. Within UK primary care research databases, such definitions have typically been represented as flat lists of Read terms, but Systematized Nomenclature of Medicine\u2014Clinical Terms (SNOMED CT) (a widely employed international reference terminology) enables the use of relationships between concepts, which could facilitate the phenotyping process. We implemented SNOMED CT-based phenotyping approaches and investigated their performance in the CPRD Aurum primary care database.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We developed SNOMED CT phenotype definitions for 3 exemplar diseases: diabetes mellitus, asthma, and heart failure, using 3 methods: \u201cprimary\u201d (primary concept and its descendants), \u201cextended\u201d (primary concept, descendants, and additional relations), and \u201cvalue set\u201d (based on text searches of term descriptions). We also derived SNOMED CT codelists in a semiautomated manner for 276 disease phenotypes used in a study of health across the lifecourse. Cohorts selected using each codelist were compared to \u201cgold standard\u201d manually curated Read codelists in a sample of 500\u00a0000 patients from CPRD Aurum.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>SNOMED CT codelists selected a similar set of patients to Read, with F1 scores exceeding 0.93, and age and sex distributions were similar. The \u201cvalue set\u201d and \u201cextended\u201d codelists had slightly greater recall but lower precision than \u201cprimary\u201d codelists. We were able to represent 257 of the 276 phenotypes by a single concept hierarchy, and for 135 phenotypes, the F1 score was greater than 0.9.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>SNOMED CT provides an efficient way to define disease phenotypes, resulting in similar patient populations to manually curated codelists.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocac158","type":"journal-article","created":{"date-parts":[[2022,9,10]],"date-time":"2022-09-10T00:07:40Z","timestamp":1662768460000},"page":"222-232","source":"Crossref","is-referenced-by-count":14,"title":["Translating and evaluating historic phenotyping algorithms using SNOMED CT"],"prefix":"10.1093","volume":"30","author":[{"given":"Musaab","family":"Elkheder","sequence":"first","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"}]},{"given":"Arturo","family":"Gonzalez-Izquierdo","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"},{"name":"Health Data Research UK , London, UK"}]},{"given":"Muhammad","family":"Qummer Ul Arfeen","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"}]},{"given":"Valerie","family":"Kuan","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9077-4741","authenticated-orcid":false,"given":"R Thomas","family":"Lumbers","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"},{"name":"Barts Health NHS Trust , London, UK"},{"name":"University College London Hospitals NHS Trust , London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9612-7791","authenticated-orcid":false,"given":"Spiros","family":"Denaxas","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"},{"name":"Health Data Research UK , London, UK"},{"name":"British Heart Foundation Data Science Centre , London, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8907-5724","authenticated-orcid":false,"given":"Anoop D","family":"Shah","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"},{"name":"University College London Hospitals NHS Trust , London, UK"}]}],"member":"286","published-online":{"date-parts":[[2022,9,9]]},"reference":[{"key":"2023011811025542000_ocac158-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbi.2017.04.010","article-title":"Clinical code set engineering for reusing EHR data for research: a 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