Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.
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| Title: | Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis. |
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| Authors: | Hendrix N; Center for Professionalism and Value in Health Care, American Board of Family Medicine, Washington, District of Columbia, United States of America., Parikh RV; Department of Epidemiology and Population Health, Stanford School of Medicine, Palo Alto, California, United States of America., Taskier M; Center for Professionalism and Value in Health Care, American Board of Family Medicine, Washington, District of Columbia, United States of America., Walter G; Robert Graham Center, American Academy of Family Physicians, Washington, District of Columbia, United States of America., Rochlin I; Inform and Disseminate Division, Office of Public Health Data, Surveillance, and Technology, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America., Saydah S; Coronavirus and Other Respiratory Viruses Division, National Center for Immunizations and Respiratory Disease, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America., Koumans EH; Coronavirus and Other Respiratory Viruses Division, National Center for Immunizations and Respiratory Disease, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America., Rincón-Guevara O; Inform and Disseminate Division, Office of Public Health Data, Surveillance, and Technology, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America., Rehkopf DH; Department of Epidemiology and Population Health, Stanford School of Medicine, Palo Alto, California, United States of America., Phillips RL; Center for Professionalism and Value in Health Care, American Board of Family Medicine, Washington, District of Columbia, United States of America. |
| Source: | PloS one [PLoS One] 2025 May 16; Vol. 20 (5), pp. e0324017. Date of Electronic Publication: 2025 May 16 (Print Publication: 2025). |
| Publication Type: | Journal Article; Multicenter Study; Observational Study |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0324017 |
