ArticleBMC medicine2025
Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care.
Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Risk factors of post-COVID-19 symptoms, a cross-sectional study in patients with asthma and chronic obstructive lung disease.NPJ primary care respiratory medicine · 2026Article
- Long COVID and risk of incident cardiovascular disease: a prospective cohort study using the Multimorbidity Integrated Registry Across Care Levels in Stockholm (MIRACLE-S) cohort.EClinicalMedicine · 2026Article
- Identifying risk factors of post-COVID-19 conditions with machine learning and deep learning algorithms.Global epidemiology · 2025Article
- Taste dysfunction in Long COVID.bioRxiv : the preprint server for biology · 2025Article
- Prevalence of depression, anxiety, fatigue, and headache before and after long COVID onset: a case-control study in the total population of Region Stockholm.BMC medicine · 2025Article
- Development and evaluation of a machine learning prediction model for short-term mortality in patients with diabetes or hyperglycemia at emergency department admission.Cardiovascular diabetology · 2025Article
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Authors and funding
13 authors.
Funding
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Abstract
backgroundThe aim of our study was to determine whether the application of machine learning could predict PASC by using diagnoses from primary care and prescribed medication 1 year prior to PASC diagnosis.
methodsThis population-based case-control study included subjects aged 18-65 years from Sweden. 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) ≥ 1% showed were considered predictive. Odds ratios of marginal effects (OR
resultsThe study included 47,568 PASC cases and controls. More females (n = 5113) than males (n = 2815) were diagnosed with PASC. Key predictive factors identified in both sexes included prior hospitalization due to acute COVID- 19 (NRI 16.1%, OR
conclusionsMachine 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.
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