将非个体患者特征集成到医院发病细菌病的机器学习模型中
M Cristina Vazquez Guillamet1,2, Jingwen Zhang3, Alice Bewley1
1Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St Louis, Missouri.
非患者因素,如房间占用率和医疗保健工作者互动,显著改善机器学习模型,用于预测医院发病的细菌病和真菌病 (HOB). 这些发现凸显了环境和社会因素在预防医院感染方面的重要性.
科学领域:
- 传染病流行病学 传染病流行病学
- 医疗保健中的机器学习
- 在医院获得的感染.
背景情况:
- 医院发病的细菌病和真菌病 (HOB) 是住院护理的重大并发症.
- 预测和预防HOB对于患者的安全和结果至关重要.
研究的目的:
- 评估机器学习模型中非个体患者特征的实用性,用于预测HOB.
- 评估患者相互作用和医疗保健工作者 (HCW) 暴露对HOB风险的贡献.
主要方法:
- 一项使用学术医院电子健康记录的预后研究.
- 发展梯度增强模型,包括预测和因果方法.
- 工程非患者特征,如房间占用历史和HCW接触率.
主要成果:
- 与仅针对患者的模型 (AUROC,0.85;AUPRC,0.13) 相比,包含非患者特征的模型显示出更好的预测性能 (AUROC,0.88;AUPRC,0.20).
- 特定的非患者因素,包括房间居民的先前治疗和每天的平均HCW,与增加HOB的可能性有关.
- 因果建模证实了这些非患者特征与HOB之间的关联.
结论:
- 非个体患者特征是机器学习模型的宝贵补充,用于HOB预测.
- 整合环境和社会相互作用数据可以增强对HOB的综合分析和预防.
- 这些发现支持一种更全面的方法来理解和缓解医院获得的感染.
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