Process for selection of the study cohort.

<div><p>Objectives</p><p>Accurately predicting the mortality risk of hypothermia patients is crucial for clinical decision-making, offering ample time for physicians to intervene. However, existing methods are invasive and difficult to implement in pre-hospital settings.</...

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Main Author:	Chunliang Jiang (22477803) (author)
Other Authors:	GuoFeng Ru (22477806) (author), GuanJun Liu (22477809) (author), Huiquan Wang (13762) (author), Xin Ma (37268) (author), JiaMeng Xu (22477812) (author), YiJing Fu (22477815) (author), Jing Yuan (217967) (author), Guang Zhang (766712) (author)
Published:	2025
Subjects:	Medicine Developmental Biology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified offering ample time machine learning method machine learning approach 840 &# 8211 minimal feature subsets entire feature subset offer decision support mortality prediction method comprising 360 non auc values increased mortality prediction model hypothermia patients based 700 hypothermia patients invasive physiological parameters hypothermia patients temperature feature model training invasive parameters hypothermic patients clinical decision predicting mortality mortality risk hour mortality xlink "> xgboost models vital importance using non remote areas predicting 72 overall features existing methods disaster sites demonstrating robustness center validation 125 hospitals 122 hospitals 039 compared
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Process for selection of the study cohort.

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