人工智能预测 COVID-19 患者死亡率的准确性：系统评价和荟萃分析。
The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis.
机构信息
Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, No. 150 Haping Rd, Nangang District, Harbin, 150081, China.
出版信息
BMC Med Inform Decis Mak. 2023 Aug 9;23(1):155. doi: 10.1186/s12911-023-02256-7.
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.
背景
本文旨在系统评估人工智能在预测 COVID-19 患者死亡率方面的应用价值。
方法
系统检索 PubMed、Embase、Web of Science、CNKI、万方、中国生物医学文献、维普数据库，检索时限均为建库至 2022 年 10 月，收集评估人工智能对 COVID-19 患者死亡率预测效果的研究。根据纳入和排除标准筛选文献，采用 QUADAS-2 工具评价纳入研究的质量。采用 Review Manager 5.3、Stata 16.0 和 Meta-DiSc 1.4 统计软件对纳入研究进行统计学分析。本项荟萃分析已在 PROSPERO（CRD42022315158）注册。
结果
共纳入 2193 篇文献，23 项研究涉及 25 个人工智能模型符合纳入标准。其中，18 项研究明确提及了训练集和测试集，5 项研究未明确分组。在训练集中，汇总敏感度为 0.93[0.87,0.96]，汇总特异度为 0.94[0.87,0.97]，ROC 曲线下面积为 0.98[0.96,0.99]。在验证集中，汇总敏感度为 0.84[0.78,0.88]，汇总特异度为 0.89[0.85,0.92]，ROC 曲线下面积为 0.93[1.00,0.00]。亚组分析显示，人工智能模型 KNN、SVM、ANN、RF 和 XGBoost 的 SROC 曲线下面积分别为 0.98、0.98、0.94、0.92 和 0.91。Deeks 漏斗图表明，本研究不存在明显的发表偏倚（P>0.05）。
结论
人工智能模型在预测 COVID-19 患者死亡率方面具有较高的准确性，具有较高的预后价值。其中，KNN、SVM、ANN、RF、XGBoost 等模型的准确性最高。
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