Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings
This study successfully developed an AI mortality prediction model for SICH patients, with XGBoost exhibiting superior performance. The model, incorporating 18 key features, has been integrated into clinical practice assisting clinicians in treatment decisions and communication with patients' families.
Authors
- Kuan-Chi Tu
- Che-Chuan Wang (ORCID: https://orcid.org/0009-0000-7928-0882)
- Jinn‐Rung Kuo (ORCID: https://orcid.org/0000-0002-7820-8050)
- Nai-Ching Chen (ORCID: https://orcid.org/0000-0002-8534-8705)
- Chia‐Jung Chen (ORCID: https://orcid.org/0000-0002-5364-7209)
- Xiao-Han Vivian Yap
- Chung‐Feng Liu (ORCID: https://orcid.org/0000-0001-6698-0273)
- Tee-Tau Eric Nya
Institutions
- National Sun Yat-sen University (TW)
- Chi Mei Medical Center (TW)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1186/s12911-025-02984-y
- Citations
- 3
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 4.17