The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis

作者：Yu Xin, Hongxu Li, Yuxin Zhou, Qing Yang, Wenjing Mu, Xiao Han, Zipeng Zhuo, Hongyu Liu, Hongyu Liu, Hongying Wang, Xutong Qu, Changsong Wang, Haitao Liu, Haitao Liu, Kaijiang Yu · 发表于：BMC Medical Informatics and Decision Making · 年份：2023 · DOI：10.1186/s12911-023-02256-7 · 被引用次数：14 · 研究领域：Artificial Intelligence in Healthcare and Education、COVID-19 diagnosis using AI、COVID-19 Clinical Research Studies

BACKGROUND: The purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients. METHODS: The PubMed, Embase, Web of Science, CNKI, Wanfang, China Biomedical Literature, and VIP databases were systematically searched from inception to October 2022 to identify studies that evaluated the predictive effects of artificial intelligence on mortality among COVID-19 patients. The retrieved literature was screened according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the QUADAS-2 tools. Statistical analysis of the included studies was performed using Review Manager 5.3, Stata 16.0, and Meta-DiSc 1.4 statistical software. This meta-analysis was registered in PROSPERO (CRD42022315158). FINDINGS: Of 2193 studies, 23 studies involving a total of 25 AI models met the inclusion criteria. Among them, 18 studies explicitly mentioned training and test sets, and 5 studies did not explicitly mention grouping. In the training set, the pooled sensitivity was 0.93 [0.87, 0.96], the pooled specificity was 0.94 [0.87, 0.97], and the area under the ROC curve was 0.98 [0.96, 0.99]. In the validation set, the pooled sensitivity was 0.84 [0.78, 0.88], the pooled specificity was 0.89 [0.85, 0.92], and the area under the ROC curve was 0.93 [1.00, 0.00]. In the subgroup analysis, the areas under the summary receiver operating characteristic (SROC) curves of the artificial...