Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation
A boosted tree machine learning model was able to identify hospitalized patients at elevated risk for delirium in the next 24 hours. This may allow for automated delirium risk screening and more precise targeting of proven and investigational interventions to prevent delirium.
Authors
- Manohar Ghanta (ORCID: https://orcid.org/0009-0004-8488-3644)
- Kendrick M. Shaw (ORCID: https://orcid.org/0000-0001-6793-9600)
- Yu‐Ping Shao
- Oluwaseun Akeju
- Michael Brandon Westover (ORCID: https://orcid.org/0000-0003-4803-312X)
- Eyal Y. Kimchi (ORCID: https://orcid.org/0000-0003-4327-1102)
- Timothy T. Houle (ORCID: https://orcid.org/0000-0001-9584-5580)
- Valdery Moura (ORCID: https://orcid.org/0000-0001-5735-9143)
Institutions
- Northwestern University (US)
- Beth Israel Deaconess Medical Center (US)
- Harvard University (US)
- Massachusetts General Hospital (US)
- Center for Pain and the Brain (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2024-12-25
- DOI
- https://doi.org/10.2196/60442
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00