Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU
This study develops a machine learning-based calculator to predict the probability of in-hospital mortality among patients with ischemic stroke in ICU. The calculator has the potential to guide clinical decision-making and improve the care of patients with ischemic stroke by identifying patients at a higher risk of in-hospital mortality.
Authors
- Cheng Wu (ORCID: https://orcid.org/0000-0003-2707-6163)
- Xiaoying Bi (ORCID: https://orcid.org/0000-0001-5784-7294)
- Yanfang Zhao (ORCID: https://orcid.org/0000-0001-9892-6079)
- Z. L. Wang (ORCID: https://orcid.org/0009-0004-9515-7947)
- Ronghui Zhu (ORCID: https://orcid.org/0000-0002-2253-9539)
- Qian He (ORCID: https://orcid.org/0000-0002-2866-9721)
- Xiao Luo
- Binghan Li (ORCID: https://orcid.org/0009-0001-9788-7597)
- Yaoyong Tai
Institutions
- University of Shanghai for Science and Technology (CN)
- Second Military Medical University (CN)
- Changhai Hospital (CN)
- Shanghai University of Medicine and Health Sciences (CN)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2025-03-09
- DOI
- https://doi.org/10.1016/j.ijmedinf.2025.105874
- Citations
- 14
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 18.96