在COVID-19大流行中,AI预测模型的死亡率和重症监护室的入院率:12,000名患者的回顾性人口队列研究
Jose Manuel Ruiz Giardin1,2, Óscar Garnica3, Nieves Mesa Plaza4
1Medicina Interna-Infecciosas, Hospital Universitario de Fuenlabrada, Fuenlabrada, Spain.
Journal of medical Internet research
|July 10, 2025
概括
人工智能模型现在可以使用初始医疗数据预测COVID-19患者死亡率和重症监护室 (ICU) 入院风险. 这些人工智能工具为早期临床干预提供了跨越大流行浪潮的稳定,个性化的预测.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 流行病学 流行病学
背景情况:
- 尽管已知风险因素,预测COVID-19患者的结果仍然是一个挑战.
- 临床医生很难识别具有类似风险因素的个体,这些个体将经历严重疾病或死亡.
研究的目的:
- 开发一个个性化的人工智能 (AI) 模型,用于预测SARS-CoV-2感染患者的死亡率和重症监护室 (ICU) 入院风险.
- 为了在初始医学评估期间,在进行任何治疗之前,能够预测风险.
主要方法:
- 追溯性,观察性,基于人群的研究 (2020年2月至2023年1月),包括11,975名患者.
- 使用Python和scikit-learn的随机森林和沙普利增量解释 (SHAP) 方法.
- 模型在4758名患者中接受了培训,并在6977名患者中得到了验证,包括各种SARS-CoV-2变体,疫苗接种状态和再感染.
主要成果:
- 死亡率预测模型实现了0.92的曲线下面面积 (AUC);ICU入院模型实现了0.89.8的AUC.
- 死亡率的关键预测因素包括年龄,查尔森并发症指数和短睡.
- 模型在所有大流行浪潮中都表现出稳定性,对于死亡率 (0.98) 和ICU入院 (0.98) 均具有高负预测值 (NPV).
结论:
- 人工智能模型在不同的SARS-CoV-2流行浪潮中显示出一致的性能.
- 这些人工智能工具具有个性化医疗服务的潜力,有助于早期预防和预测性临床决策.
- 这些模型可以帮助临床医生在公共卫生紧急情况下管理患者的风险.
关键词:
在 COVID-19 疫情中,这就是SARS-CoV-2病毒.人工智能的人工智能是人工智能.死亡死亡死亡死亡死亡死亡重症监护病房的重症监护病房是一个重症监护病房.死亡率 死亡率人口研究 人口研究预测模型是一个预测模型.随机的森林随机的森林更多相关视频
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