可解释的人工智能用于预测住院COVID-19患者的心血管事件
Milena Soriano Marcolino1,2, Isabella Viana Gomes Schettini3,4, Guilherme Fonseca do Nascimento5
1Medical School and University Hospital, Universidade Federal de Minas Gerais, Av. Professor Alfredo Balena, 190, room 246, Belo Horizonte, Brazil.
BMC infectious diseases
|November 13, 2025
概括
人工智能 (AI) 模型在住院COVID-19患者的心血管事件预测中显示出适度的准确性,但由于阶级不平衡,难以检测罕见事件. 确定的主要预测因素包括年龄,尿素,血小板计数和氧和.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- COVID-19与心血管并发症的增加有关.
- 人工智能 (AI) 在这些情况下具有早期风险预测的潜力.
研究的目的:
- 开发和评估人工智能模型,用于识别住院COVID-19患者的心血管事件预测因素.
- 评估人工智能模型在预测复合心血管结果方面的表现.
主要方法:
- 从25家医院对10,700名成年COVID-19患者进行了回顾性分析.
- 使用人口,临床,实验室和社会经济数据开发两种LightGBM AI模型.
- 使用准确度,宏F1,回忆,精度和AUROC的性能评估;用于预测器识别的SHAP值;用于类不平衡的随机过量抽样.
主要成果:
- 这两种人工智能模型都实现了中度的区别 (AUROC ~ 0.75) 和高整体精度 (~94.5%).
- 显著的阶级失衡导致了较低的宏观F1分数和极低的F1分数 (心血管事件,<5.2%).
- 即使采用过量抽样,少数阶级的表现仍然有限 (最大F1得分为21.5%),提醒能力以牺牲精度为代价. 关键预测因素:年龄,尿素,血小板计数,SatO2/FiO2.2.
结论:
- 人工智能模型表现出适度的预测能力,但由于严重的阶级失衡,在COVID-19患者中检测心血管事件方面受到限制.
- 少数群体的低F1分数凸显了识别罕见事件的挑战.
- 年龄,尿素,血小板数和SatO2 / FiO2成为心血管并发症的重要预测因素.
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