Leveraging near-real-time patient and population data to incorporate fluctuating risk of severe COVID-19: development and prospective validation of a personalised risk prediction tool
This work was supported by the VA Boston Cooperative Studies Programme. WBE was supported by VA HSR&D IIR 20-076; VA HSR&D IIR 20-101; VA National Artificial Intelligence Institute.
Authors
- Austin D Vo
- Nhan Do (ORCID: https://orcid.org/0000-0001-6868-7011)
- Kaitlin N. Swinnerton (ORCID: https://orcid.org/0000-0002-7429-9960)
- Jennifer La (ORCID: https://orcid.org/0000-0003-2266-0221)
- Westyn Branch‐Elliman (ORCID: https://orcid.org/0000-0002-9658-5124)
- Danne C. Elbers (ORCID: https://orcid.org/0000-0002-0454-0173)
- Nathanael R. Fillmore (ORCID: https://orcid.org/0000-0002-8058-3423)
- Mary T. Brophy (ORCID: https://orcid.org/0000-0003-2314-4368)
- Paul A. Monach (ORCID: https://orcid.org/0000-0003-4937-0515)
Institutions
- Harvard University (US)
- University of California, Los Angeles (US)
- VA Boston Healthcare System (US)
- Eastern Cooperative Oncology Group (US)
- Boston VA Research Institute (US)
- Dana-Farber Cancer Institute (US)
- Center for the Study of Healthcare Provider Behavior (US)
- VA Greater Los Angeles Healthcare System (US)
Publication Details
- Journal
- EClinicalMedicine
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1016/j.eclinm.2025.103114
- Citations
- 2
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 4.96
Funders
- Health Services Research and Development