基于机器学习的临床决策支持,用于感染风险预测和预测.
Ting Feng1, David P Noren1, Chaitanya Kulkarni2
1Philips Research North America, Cambridge, MA, United States.
这项研究开发了一个人工智能工具,可以在症状出现前几个小时预测与医疗相关的感染 (HAI). 该模型使用生命体征和实验室数据提供早期警告,改善患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床决策支持 临床决策支持
背景情况:
- 医疗相关感染 (HAI) 对住院患者和医疗保健系统构成重大风险.
- 早期检测和干预对于管理HAI至关重要.
- 当前的诊断方法可能无法在感染临床显现之前识别感染.
研究的目的:
- 开发和验证一种机器学习模型,用于在明显的症状出现之前预测HAI.
- 创建一个临床决策支持工具,用于积极的患者评估.
- 为了使住院患者早期诊断和治疗感染.
主要方法:
- 在一个大型的回顾性医院数据集上利用基于集体的增强决策树.
- 提取了36782名医疗保健相关感染患者的数据集.
- 利用生命体征,实验室测量和人口统计数据来预测HAI风险.
主要成果:
- 在临床怀疑前1小时,表现最好的模型实现了0.88的交叉验证AUC.
- 该模型在怀疑之前的48小时内保持了AUC>0.85.
- 使用较少特征的缩小模型在怀疑前1小时仍然达到0.86的AUC,优于现有方法.
结论:
- 预测模型有效地汇总生理数据,提供连续感染风险评分.
- 这种工具可以在医院部署,以提前警告患者因感染而恶化.
- 早期预测HAI可以导致及时干预和改善患者护理.
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