机器学习模型的开发和验证,通过使用电子健康记录数据来预测MDRO殖民或ICU入院时的感染
Antimicrobial resistance and infection control
|July 6, 2024
概括
机器学习模型现在可以在重症监护室 (ICU) 住院的早期确定患有多重耐药生物体 (MDRO) 高风险的患者. 这有助于控制感染传播和改善患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 传染病流行病学 传染病流行病学
背景情况:
- 耐多药生物体 (MDRO) 构成了严重的公共卫生挑战,重症监护病房 (ICU) 是它们扩散的主要场所,因为抗菌素的使用和耐药性很高.
- 早期识别患有MDRO高风险的患者对于预防传播,改善患者的治疗结果和维护ICU卫生至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,以便在入院时预测ICU患者的MDRO风险.
- 确定关键的临床和生化预测因子与MDRO风险在ICU停留的初始阶段.
主要方法:
- 利用来自两个大型数据集的患者数据:PLAGH-ICU和MIMIC-IV,分析ICU入院后24小时内的变量.
- 应用机器学习算法用于MDRO殖民或感染的早期检测,使用AUROC和时间/外部验证评估模型性能.
主要成果:
- 评估了3536名 (PLAGH-ICU) 和34923名 (MIMIC-IV) 患者,MDRO患病率分别为11.96%和8.81%.
- 在时间验证中,PLAGH-ICU的AUROC值为0.786,MIMIC-IV模型的AUROC值为0.744;外部验证显示性能变化.
- 确定生物化学标志物和ICU前住院时间作为MDRO风险的重要预测因素.
结论:
- 开发的ML模型显示了在ICU患者早期MDRO风险识别的前景.
- 为了广泛应用,需要在不同的临床环境中进行持续的改进和验证.
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