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Background <p>The 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&#xa0;year prior to PASC diagnosis.</p> Methods <p>This 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<sub>ME</sub>) were calculated.</p> Results <p>The study included 47,568 PASC cases and controls. More females (<i>n</i> = 5113) than males (<i>n</i> = 2815) were diagnosed with PASC. Key predictive factors identified in both sexes included prior hospitalization due to acute COVID- 19 (NRI 16.1%, OR<sub>ME</sub> 18.8 for females; NRI 41.7%, OR<sub>ME</sub> 31.6 for males), malaise and fatigue (NRI 14.5%, OR<sub>ME</sub> 4.6 for females; NRI 11.5%, OR<sub>ME</sub> 7.9 for males), and post-viral and related fatigue syndromes (NRI 10.1%, OR<sub>ME</sub> 21.1 for females; NRI 6.4%, OR<sub>ME</sub> 28.4 for males).</p> Conclusions <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.</p>

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Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月30日

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  • Seika Lee,
  • Marta A. Kisiel,
  • Pia Lindberg,
  • Åsa M. Wheelock,
  • Anna Olofsson,
  • Julia Eriksson,
  • Judith Bruchfeld,
  • Michael Runold,
  • Lars Wahlström,
  • Andrei Malinovschi,
  • Christer Janson,
  • Caroline Wachtler,
  • Axel C. Carlsson

摘要

Background

The 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.

Methods

This 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 (ORME) were calculated.

Results

The 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%, ORME 18.8 for females; NRI 41.7%, ORME 31.6 for males), malaise and fatigue (NRI 14.5%, ORME 4.6 for females; NRI 11.5%, ORME 7.9 for males), and post-viral and related fatigue syndromes (NRI 10.1%, ORME 21.1 for females; NRI 6.4%, ORME 28.4 for males).

Conclusions

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.