在COVID-19患者的选中使用人工智能
Yuri Oliveira1, Iêda Rios2, Paula Araújo3
1School of Medicine, University of Brasilia, Brasilia, Brazil.
Frontiers in artificial intelligence
|January 2, 2025
概括
机器学习模型可以仅使用入院数据来预测严重的COVID-19结果. 这种人工智能驱动的方法有助于在流行病期间有效地对病人进行分拣和资源分配.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 人工智能在医学中的应用
背景情况:
- 随着COVID-19成为全球重大健康危机,需要患者管理的预测工具.
- 识别患有严重COVID-19风险的患者对于优化医疗保健资源配置至关重要.
研究的目的:
- 评估机器学习 (ML) 算法,用于预测严重的COVID-19结果.
- 评估使用现有入院数据进行患者风险分层的可行性.
主要方法:
- 利用了来自呼吸系统综合征住院患者的前性多中心队列数据.
- 根据初始临床数据,应用各种ML算法来预测严重的COVID-19病例 (ICU入院,输管或死亡).
- 专注于来自低成本临床检查和实验室测试的数据.
主要成果:
- 在预测严重的COVID-19病例方面实现了80%的准确性和91%的ROC曲线下的区域 (AUC).
- 显示了87%的正预测值和82%的负预测值.
- 该模型显示低假阳性率,表明可靠的预测.
结论:
- 使用医院入院数据的基于人工智能的预测系统是可行的有效的患者分拣.
- 这种方法可以在流行病情景中优化资源配置.
- 该研究强调了可访问的临床数据在开发强大的预测模型方面的潜力.
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