Enhancing COVID-19 Screening Models With Epidemiological and Mobility Features: Machine-Learning Model Study
This study demonstrated the potential of using mobility and epidemic data, such as location information and epidemic data, in combination with patient symptom data to improve the accuracy of ML models for diagnosing COVID-19. Considering additional contextual information can enhance the ability to screen for COVID-19.
Authors
- Do‐Hyung Lee (ORCID: https://orcid.org/0000-0002-2589-9177)
- Hyo Jung Kim (ORCID: https://orcid.org/0000-0002-5098-2116)
- Hyunwoo Choo (ORCID: https://orcid.org/0000-0003-2981-0958)
- Duhun Lee
- Jiwoo Lee (ORCID: https://orcid.org/0000-0003-2539-1098)
- Christina Kim (ORCID: https://orcid.org/0000-0003-4404-215X)
- Soo-Yong Shin
- Eonji Kim (ORCID: https://orcid.org/0009-0001-3386-9226)
- Namsoo Oh
Institutions
- China Mobile (China) (CN)
- Samsung (South Korea) (KR)
- Sungkyunkwan University (KR)
- Catholic University of Korea (KR)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-08-08
- DOI
- https://doi.org/10.2196/54956
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Bill and Melinda Gates Foundation