人工智能在预测COVID-19患者死亡率方面的准确性:系统性审查和元分析
Yu Xin1,2, Hongxu Li1,2, Yuxin Zhou1,2
1Department of Critical Care Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang Province, China.
BMC medical informatics and decision making
|August 9, 2023
概括
人工智能 (AI) 模型在预测COVID-19患者死亡率方面表现出高准确性. 像KNN和SVM这样的特定AI模型显示了患者结果的最高预后值.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 由于COVID-19的流行,需要在患者预后方面取得快速进展.
- 准确预测死亡风险对于有效的资源配置和患者管理至关重要.
- 人工智能 (AI) 为开发复杂的预测模型提供了潜力.
研究的目的:
- 系统地评估人工智能 (AI) 在预测COVID-19患者死亡率中的应用价值.
- 评估各种AI模型的预后性能,以预测COVID-19死亡率.
主要方法:
- 在主要数据库 (PubMed,Embase,Web of Science等) 进行了系统的文献搜索. 截至 2022 年 10 月.
- 包括的研究被选,并使用QUADAS-2工具评估其质量.
- 通过使用Review Manager 5.3,Stata 16.0和Meta-DiSc 1.4进行了元分析;注册在PROSPERO (CRD42022315158) 中.
主要成果:
- 涉及25个人工智能模型的23项研究符合纳入标准.
- 在训练组中,聚合灵敏度和特异性分别为0.93和0.94 (AUC=0.98).
- 验证组中的聚合灵敏度和特异性分别为0.84和0.89 (AUC=0.93).
- 小组分析显示KNN,SVM,ANN,RF和XGBoost模型的高性能.
结论:
- 人工智能模型在预测COVID-19患者死亡率方面表现出显著的准确性和预后价值.
- 像KNN,SVM,ANN,RF和XGBoost这样的模型表现出特别高的预测准确度.
- 人工智能应用有望改善管理COVID-19患者的临床决策.
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