Machine Learning for Predicting Critical Events Among Hospitalized Children
This retrospective cohort study describes the development of a novel hospitalwide model for continuously predicting the risk of critical events through the entirety of a child's stay. The model facilitated a unified framework for risk assessment in a pediatric hospital.
Authors
- Anoop Mayampurath (ORCID: https://orcid.org/0000-0002-3010-6960)
- L. Nelson Sanchez‐Pinto (ORCID: https://orcid.org/0000-0002-7434-6747)
- Dana P. Edelson
- Nicholas Kuehnel (ORCID: https://orcid.org/0000-0002-3638-8911)
- Priti Jani
- Fereshteh S. Bashiri (ORCID: https://orcid.org/0000-0002-0153-4958)
- Maya Dewan (ORCID: https://orcid.org/0000-0002-9966-3845)
- Majid Afshar (ORCID: https://orcid.org/0000-0002-6368-4652)
- Kyle A. Carey (ORCID: https://orcid.org/0000-0002-5038-6074)
- Matthew M. Churpek (ORCID: https://orcid.org/0000-0002-4030-5250)
- J. Fitzgerald
- Huan Liang (ORCID: https://orcid.org/0009-0008-5126-5144)
- Emily Gilbert
- Sierra Strutz
Institutions
- Lurie Children's Hospital (US)
- Loyola University Medical Center (US)
- University of Wisconsin–Madison (US)
- University of Chicago (US)
- University of Cincinnati (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.13149
- Citations
- 10
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 22.53