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

BMC Medical Informatics and Decision Making

Research

Hongyu Liu¹  Xutong Qu¹  Qing Yang²  Zipeng Zhuo²  Hongying Wang²  Haitao Liu²  Wenjing Mu²  Kaijiang Yu³  Yuxin Zhou⁴  Yu Xin⁴  Changsong Wang⁴  Hongxu Li⁴  Han Xiao⁵ 

[1] Department of Anesthesiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, 150081, Harbin, China;Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, 150081, Harbin, China;Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, 150001, Harbin, Heilongjiang Province, China;Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, 150001, Harbin, Heilongjiang Province, China;Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, 150081, Harbin, China;Departments of Pharmacy and Cardiology, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, 150081, Harbin, China;

关键词: Artificial intelligence;    COVID-19;    Mortality;    meta-analysis;   

DOI  :  10.1186/s12911-023-02256-7

 received in 2022-08-01, accepted in 2023-08-02,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients.MethodsThe 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).FindingsOf 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 intelligence models KNN, SVM, ANN, RF and XGBoost were 0.98, 0.98, 0.94, 0.92, and 0.91, respectively. The Deeks funnel plot indicated that there was no significant publication bias in this study (P > 0.05).InterpretationArtificial intelligence models have high accuracy in predicting mortality among COVID-19 patients and have high prognostic value. Among them, the KNN, SVM, ANN, RF, XGBoost, and other models have the highest levels of accuracy.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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