Identifying risk factors of post–COVID-19 conditions with machine learning and deep learning algorithms
Multiple machine learning and deep learning models identified the Charlson comorbidity score, age, and frequency of healthcare utilization, which may help predict the occurrence of new PCC and demonstrated the utility of the models for individualized risk prediction.
Authors
- Guohai Zhou (ORCID: https://orcid.org/0000-0002-8826-403X)
- Rongjun Shen
- Michael D. Henschel (ORCID: https://orcid.org/0000-0003-4508-276X)
- Stephen E. Schachterle (ORCID: https://orcid.org/0000-0001-6017-2670)
- Leo Russo (ORCID: https://orcid.org/0000-0001-8318-6741)
- Scott P. Kelly (ORCID: https://orcid.org/0000-0002-0375-1040)
- Xiaofeng Zhou (ORCID: https://orcid.org/0000-0002-2109-0012)
- Ling Li (ORCID: https://orcid.org/0000-0001-7704-0658)
Institutions
- Pfizer (United States) (US)
Publication Details
- Journal
- Global Epidemiology
- Published
- 2025-09-27
- DOI
- https://doi.org/10.1016/j.gloepi.2025.100221
- Primary Topic
- Long-Term Effects of COVID-19
- Type
- article
- Field-Weighted Citation Impact
- 0.00