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Stochastic gradient boosting was used to develop a predictive model using diagnoses received in primary care, hospitalization due to acute COVID- 19, and prescribed medication. The variables with normalized relative influence (NRI) \u2265\u20091% showed were considered predictive. Odds ratios of marginal effects (OR<jats:sub>ME<\/jats:sub>) were calculated.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The study included 47,568 PASC cases and controls. More females (<jats:italic>n<\/jats:italic>\u2009= 5113) than males (<jats:italic>n<\/jats:italic>\u2009= 2815) were diagnosed with PASC. Key predictive factors identified in both sexes included prior hospitalization due to acute COVID- 19 (NRI 16.1%, OR<jats:sub>ME<\/jats:sub> 18.8 for females; NRI 41.7%, OR<jats:sub>ME<\/jats:sub> 31.6 for males), malaise and fatigue (NRI 14.5%, OR<jats:sub>ME<\/jats:sub> 4.6 for females; NRI 11.5%, OR<jats:sub>ME<\/jats:sub> 7.9 for males), and post-viral and related fatigue syndromes (NRI 10.1%, OR<jats:sub>ME<\/jats:sub> 21.1 for females; NRI 6.4%, OR<jats:sub>ME<\/jats:sub> 28.4 for males).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Machine learning can predict PASC based on previous diagnoses and medications. Use of this AI method could support diagnostics of PASC in primary care and provide insight into PASC etiology.\n<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12916-025-04050-w","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T06:03:09Z","timestamp":1746079389000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care"],"prefix":"10.1186","volume":"23","author":[{"given":"Seika","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta A.","family":"Kisiel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pia","family":"Lindberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c5sa M.","family":"Wheelock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Olofsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Eriksson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judith","family":"Bruchfeld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Runold","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars","family":"Wahlstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Malinovschi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christer","family":"Janson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"Wachtler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel C.","family":"Carlsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"4050_CR1","volume-title":"In the wake of the pandemic: preparing for long COVID","author":"S Rajan","year":"2021","unstructured":"Rajan S, Khunti K, Alwan N, Steves C, MacDermott N, Morsella A, et al. 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