{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:46:48Z","timestamp":1787237208510,"version":"build-2736575974"},"reference-count":14,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000069","name":"National Institute of Arthritis and Musculoskeletal and Skin Diseases","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000069","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>We aimed to determine whether integrating concepts from the notes from the electronic health record (EHR) data using natural language processing (NLP) could improve the identification of gout flares.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using Medicare claims linked with EHR, we selected gout patients who initiated the urate\u2010lowering therapy (ULT). Patients' 12\u2010month baseline period and on\u2010treatment follow\u2010up were segmented into 1\u2010month units. We retrieved EHR notes for months with gout diagnosis codes and processed notes for NLP concepts. We selected a random sample of 500 patients and reviewed each of their notes for the presence of a physician\u2010documented gout flare. Months containing at least 1 note mentioning gout flares were considered months with events. We used 60% of patients to train predictive models with LASSO. We evaluated the models by the area under the curve (AUC) in the validation data and examined positive\/negative predictive values (P\/NPV).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We extracted and labeled 839\u2009months of follow\u2010up (280 with gout flares). The claims\u2010only model selected 20 variables (AUC\u2009=\u20090.69). The NLP concept\u2010only model selected 15 (AUC\u2009=\u20090.69). The combined model selected 32 claims variables and 13 NLP concepts (AUC\u2009=\u20090.73). The claims\u2010only model had a PPV of 0.64 [0.50, 0.77] and an NPV of 0.71 [0.65, 0.76], whereas the combined model had a PPV of 0.76 [0.61, 0.88] and an NPV of 0.71 [0.65, 0.76].<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Adding NLP concept variables to claims variables resulted in a small improvement in the identification of gout flares. Our data\u2010driven claims\u2010only model and our combined claims\/NLP\u2010concept model outperformed existing rule\u2010based claims algorithms reliant on medication use, diagnosis, and procedure codes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pds.5684","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T02:08:32Z","timestamp":1693534112000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improving the accuracy of automated gout flare ascertainment using natural language processing of electronic health records and linked Medicare claims data"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2030-3549","authenticated-orcid":false,"given":"Kazuki","family":"Yoshida","sequence":"first","affiliation":[{"name":"Division of Rheumatology, Inflammation, and Immunity, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"},{"name":"Department of Medicine, Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianrun","family":"Cai","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Inflammation, and Immunity, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"},{"name":"Department of Medicine, Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lily G.","family":"Bessette","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erin","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su Been","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke E.","family":"Zabotka","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alec","family":"Sun","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julianna M.","family":"Mastrorilli","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theresa A.","family":"Oduol","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8202-5428","authenticated-orcid":false,"given":"Daniel H.","family":"Solomon","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Inflammation, and Immunity, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"},{"name":"Department of Medicine, Harvard Medical School  Boston Massachusetts USA"},{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seoyoung C.","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Inflammation, and Immunity, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"},{"name":"Department of Medicine, Harvard Medical School  Boston Massachusetts USA"},{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rishi J.","family":"Desai","sequence":"additional","affiliation":[{"name":"Department of Medicine, Harvard Medical School  Boston Massachusetts USA"},{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine P.","family":"Liao","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Inflammation, and Immunity, Department of Medicine Brigham and Women's Hospital  Boston Massachusetts USA"},{"name":"Department of Medicine, Harvard Medical School  Boston Massachusetts USA"},{"name":"Department of Biomedical Informatics Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMcp1001124"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)00346-9"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/art.41247"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.4044"},{"key":"e_1_2_10_6_1","first-page":"552","article-title":"A security architecture for query tools used to access large biomedical databases","author":"Murphy SN","year":"2002","journal-title":"Proc AMIA 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