Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach

Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach

Background A feasible and accurate risk prediction systems for emergency department (ED) patients is urgently required. The Modified Early Warning Score (MEWS) is a wide-used tool to predict clinical outcomes in ED. Literatures showed that machine learning (ML) had better predictability in specific...

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Bibliographic Details
Main Authors: Kuan-Han Wu, Fu-Jen Cheng, Hsiang-Ling Tai, Jui-Cheng Wang, Yii-Ting Huang, Chih-Min Su, Yun-Nan Chang
Format: Article
Language:English
Published: PeerJ Inc. 2021-08-01
Series:PeerJ
Subjects:
Mortality prediction
MEWS
Machine learning
Online Access:https://peerj.com/articles/11988.pdf

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