Machine Learning Multimodal Model for Delirium Risk Stratification
This quality improvement study demonstrates the feasibility of a novel multimodal ML model to automate delirium risk stratification in live clinical practice. The model demonstrated acceptable performance in live clinical practice and may facilitate resource allocation to enhance delirium identification and care.
Authors
- Katherine Reina
- Arash Kia (ORCID: https://orcid.org/0000-0002-4699-7892)
- Prem Timsina (ORCID: https://orcid.org/0000-0002-6047-887X)
- Joseph I. Friedman (ORCID: https://orcid.org/0000-0002-0540-0121)
- Ganesh Raut (ORCID: https://orcid.org/0009-0006-7914-7065)
- Prathamesh Parchure (ORCID: https://orcid.org/0009-0001-9383-691X)
- Weijia Fu (ORCID: https://orcid.org/0009-0002-5955-3534)
- Madhu Mazumdar (ORCID: https://orcid.org/0000-0003-3965-5146)
- Robert Freeman (ORCID: https://orcid.org/0000-0003-4946-6533)
- David L. Reich (ORCID: https://orcid.org/0000-0003-0095-515X)
- Fu-Yuan Cheng (ORCID: https://orcid.org/0000-0003-2273-6089)
- Satyanarayana Cheertirala
- Josiane Joseph-Jimerson
Institutions
- Mount Sinai Hospital (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-05-07
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.8874
- Citations
- 6
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 9.27