Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis
Models that predict the risk of disease and ignore the effects of disease on downstream outcomes could be suboptimal for stratification.
Authors
- Fahad Kamran (ORCID: https://orcid.org/0000-0003-2488-8887)
- Hallie C. Prescott (ORCID: https://orcid.org/0000-0002-8442-6724)
- Nigam H. Shah (ORCID: https://orcid.org/0000-0001-9385-7158)
- Eric Horvitz (ORCID: https://orcid.org/0000-0002-8823-0614)
- Jenna Wiens (ORCID: https://orcid.org/0000-0002-1057-7722)
- Donna Tjandra (ORCID: https://orcid.org/0000-0001-7303-9123)
- Vincent X. Liu (ORCID: https://orcid.org/0000-0001-6899-9998)
- Thomas S. Valley (ORCID: https://orcid.org/0000-0002-5766-4970)
Institutions
- Microsoft (United States) (US)
- Kaiser Permanente (US)
- University of Michigan (US)
- VA Center for Clinical Management Research (US)
- Stanford University (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1093/jamia/ocaf036
- Citations
- 1
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Science Foundation
- National Institutes of Health