计算机科学
重症监护室
2019年冠状病毒病(COVID-19)
重症监护
经济短缺
数据挖掘
人工智能
医学
重症监护医学
传染病(医学专业)
疾病
病理
语言学
哲学
政府(语言学)
作者
Jing Zheng,Long-Hao Yang,Ying‐Ming Wang,Jian-Qing Gao,Kai Zhang
标识
DOI:10.1016/j.asoc.2023.110961
摘要
Given the shortage of intensive care units (ICUs) due to the coronavirus disease (COVID-19), a prediction model is essential in ensuring the availability of ICU beds. However, several challenges, such as the importance of distinguishing indicators, efficiency of ICU admission records, and the explainability and effectiveness of the prediction model, hinder the effective prediction of ICU admissions. To mitigate these challenges, an explainable decision model that uses the extended belief rule-based system is introduced to predict ICU admission. First, an indicator extraction model is proposed to measure the importance of the various indicators and obtain representative indicators. Second, a Charnes, Cooper, and Rhodes (CCR) model is constructed to measure the efficiencies of the belief rules to achieve the compact structure of an extended belief rule base. Third, a new extended belief rule-based model, optimized by parameter optimization and domain division-based rule reduction, is developed to predict ICU admission. These procedures enable the explainable decision model to adapt to big data situation, offer explanations and realize high efficiency. Finally, a case study of ICU admission during a COVID-19 outbreak is conducted to demonstrate the implementation and effectiveness of the proposed model in a comparative analysis.
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