Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model
This study proposes an ICU agitation-sedation assessment automation using machine learning, enhancing efficiency and safety. Ensemble learning improves agitation sensitivity while maintaining accuracy. Real-time monitoring and future digital integration have the potential for advancements in intensive care.
Authors
- Yucheng Wu (ORCID: https://orcid.org/0000-0002-1549-0546)
- Ruey‐Kai Sheu (ORCID: https://orcid.org/0000-0002-3014-8095)
- Pei-Yi Lin (ORCID: https://orcid.org/0009-0002-0117-4240)
- Shu-Fang Liu (ORCID: https://orcid.org/0009-0006-1637-6899)
- Lun-Chi Chen (ORCID: https://orcid.org/0000-0002-8449-7872)
- Pei-Yu Dai
- Weilin Chen (ORCID: https://orcid.org/0000-0002-4279-3534)
- Chung-Hsiung Huang (ORCID: https://orcid.org/0009-0009-4966-8216)
- Chieh‐Liang Wu (ORCID: https://orcid.org/0000-0001-5322-7426)
- Guan-Yin Lin
Institutions
- National Chung Hsing University (TW)
- Tunghai University (TW)
- Taichung Veterans General Hospital (TW)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-02-26
- DOI
- https://doi.org/10.2196/63601
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00