医学
系统回顾
梅德林
输血
数据提取
前瞻性队列研究
急诊医学
重症监护医学
机器学习
人工智能
外科
计算机科学
政治学
法学
作者
William Oakley,Sankalp Tandle,Zane Perkins,Max Marsden
标识
DOI:10.1097/ta.0000000000004385
摘要
Hemorrhage is a leading cause of preventable death in trauma. Accurately predicting a patient's blood transfusion requirement is essential but can be difficult. Machine learning (ML) is a field of artificial intelligence that is emerging within medicine for accurate prediction modeling. This systematic review aimed to identify and evaluate all ML models that predict blood transfusion in trauma.
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