Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study
We developed a machine learning model for delirium prediction in ICU patients using routinely measured variables, including physiological waveforms. Our study demonstrates the potential of the RF model in predicting delirium, with consistent performance across various validation scenarios. The model uses noninvasive variables, making it applicable to a wide range of ICU patients, with minimal additional risk.
Authors
- Dukyong Yoon (ORCID: https://orcid.org/0000-0003-1635-8376)
- Sora Kim (ORCID: https://orcid.org/0000-0001-9787-2339)
- Byung Hee Kang (ORCID: https://orcid.org/0000-0003-3204-3251)
- Chanmin Park (ORCID: https://orcid.org/0000-0002-6088-6048)
- Hyungjun Kim (ORCID: https://orcid.org/0000-0002-4040-3715)
- Changho Han (ORCID: https://orcid.org/0000-0003-4121-5465)
- Kyoungwon Jung (ORCID: https://orcid.org/0000-0001-7895-0362)
- Su Kyeong Jang (ORCID: https://orcid.org/0000-0002-0313-5491)
Institutions
- Yonsei University (KR)
- Ajou University Hospital (KR)
- Seoul University of Buddhism (KR)
- Ajou University (KR)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-04-02
- DOI
- https://doi.org/10.2196/59520
- Citations
- 11
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 17.00