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Meta-Analysis
. 2023 Aug 9;23(1):155.
doi: 10.1186/s12911-023-02256-7.

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

Yu Xin #  1   2 , Hongxu Li #  1   2 , Yuxin Zhou #  1   2 , Qing Yang #  2 , Wenjing Mu  2 , Han Xiao  3 , Zipeng Zhuo  2 , Hongyu Liu  4 , Hongying Wang  2 , Xutong Qu  4 , Changsong Wang  5   6 , Haitao Liu  7 , Kaijiang Yu  8
Affiliations
Meta-Analysis

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

Yu Xin et al. BMC Med Inform Decis Mak. .

Abstract

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 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).

Interpretation: Artificial 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.

Keywords: Artificial intelligence; COVID-19; Mortality; meta-analysis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Literature screening flowchart
Fig. 2
Fig. 2
Forest plot of the pooled sensitivity and specificity
Fig. 3
Fig. 3
Forest plot of the pooled diagnostic odds ratio
Fig. 4
Fig. 4
Forest plot of the pooled positive LR and negative LR
Fig. 5
Fig. 5
SROC of AI for the diagnosis of COVID-19 patient mortality
Fig. 6
Fig. 6
Funnel plot of studies included in the meta-analysis
See this image and copyright information in PMC

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