Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making
AI-driven prediction of ventilation duration can enhance ICU workflows, optimize resource use, and improve personalized care. SHAP-based feature selection promotes AI interpretability, aiding clinical adoption.
Authors
- Amit Mehndiratta (ORCID: https://orcid.org/0000-0001-6477-2462)
- Esha Baidya Kayal (ORCID: https://orcid.org/0000-0002-0377-826X)
- Puneet Khanna (ORCID: https://orcid.org/0000-0002-9243-9963)
- Vinay Gandhi Mukkelli (ORCID: https://orcid.org/0009-0006-6738-3167)
- Shivi Mendiratta (ORCID: https://orcid.org/0009-0004-3038-4268)
Institutions
- UNSW Sydney (AU)
- All India Institute of Medical Sciences (IN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251352988
- Citations
- 2
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.71