基于机器学习的急诊科患者住院预测。
Prediction of Hospitalization Using Machine Learning for Emergency Department Patients.
机构信息
School of Science and Technology, Hellenic Open University, Patras, Greece.
Department of Quality Control, Research and Continuing Education, Sismanogleio General Hospital, Marousi, Greece.
出版信息
Stud Health Technol Inform. 2022 May 25;294:145-146. doi: 10.3233/SHTI220422.
PMID:35612042
Abstract
The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area under the receiver operating characteristic curve (AUC ROC).
摘要
本研究旨在评估五种机器学习模型在预测急诊科患者收治或出院方面的预测能力。随机森林分类器在接受者操作特征曲线下面积（AUC ROC）方面优于其他模型。
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