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Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation
22
Zitationen
10
Autoren
2023
Jahr
Abstract
The XGBoost model accurately predicted 28-day all-cause in-hospital mortality among hypertensive ischemic or hemorrhagic stroke patients admitted to the ICU. The SHAP method can provide explicit explanations of personalized risk prediction, which can aid physicians in understanding the model.
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Autoren
Institutionen
- Chongqing Medical University(CN)
- Dalian Medical University(CN)
- Second Affiliated Hospital of Chongqing Medical University(CN)
- Guangxi University of Chinese Medicine(CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University(CN)
- Capital Medical University(CN)
- Beijing Shijitan Hospital(CN)
- Army Medical University(CN)
- Daping Hospital(CN)
- Macau University of Science and Technology(MO)
- Hangzhou Dianzi University(CN)
- Xuzhou Medical College(CN)