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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.

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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Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.利用机器学习和自然语言处理提高急诊科分诊性能的系统评价。
BMC Emerg Med. 2024 Nov 18;24(1):219. doi: 10.1186/s12873-024-01135-2.
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The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.人工智能在急诊护理中的应用；一项范围综述
Arch Acad Emerg Med. 2023 May 11;11(1):e38. doi: 10.22037/aaem.v11i1.1974. eCollection 2023.