Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care - Lee, Seika; Kisiel, Marta A.; Lindberg, Pia; Wheelock, Åsa M.; Olofsson, Anna; Eriksson, Julia; Bruchfeld, Judith; Runold, Michael; Wahlström, Lars; Malinovschi, Andrei; Janson, Christer; Wachtler, Caroline; et al. (BMC medicine, 2025)
Keywords: Adolescent; Adult; Aged; Cardiovascular disease; Case-Control Studies; Chronic illnesses; Codes; Complications; Coronaviruses; COVID- 19; COVID-19 - complications; COVID-19 - diagnosis; COVID-19 - epidemiology; Decision trees; Development and progression; Diabetes; Drugs; Evidence-based medicine; Fatigue; Female; Females; Hospitalization; Hospitalization - statistics & numerical data; Humans; Learning algorithms; Machine Learning; Male; Males; Middle Aged; Missing data; Population; Population studies; Post-Acute COVID-19 Syndrome; Post-acute sequelae of COVID-19 (PASC); Prediction models; Primary care; Primary Health Care; Regression analysis; Risk Assessment - methods; Risk Factors; SARS-CoV-2; Stochastic gradient boosting; Sweden - epidemiology; Type 2 diabetes; Variables; Young Adult; Sweden; COVID- 19; Primary care; Stochastic gradient boosting; Machine learning; Post-acute sequelae of COVID- 19 (PASC)