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使用机器学习和基于规则的分类模型对手术部位感染进行半自动监测

Américo Agostinho¹, Etienne Chalot¹, Daniel Teixeira¹

¹Infection Control Programme and WHO Collaborating Centre on Infection Prevention and Control and Antimicrobial Resistance, Geneva University Hospitals, Geneva, Switzerland.

NPJ digital medicine

|October 17, 2025

概括

机器学习模型可以帮助更有效地检测手术部位感染 (SSI). 这些自动化工具减少了监测医疗保健相关感染的手工工作量.

科学领域:

与医疗保健相关的感染
医疗信息学医学信息学
机器学习在医学中的应用

背景情况:

手术部位感染 (SSI) 是一种常见的与医疗相关的感染.
传统的SSI监控方法是劳动密集型的.
需要有效和准确的监控方法.

研究的目的:

开发和评估机器学习 (ML) 和基于规则的模型,用于深度和器官/空间SSI的半自动检测.
评估这些模型在灵敏度,工作量减少,AUROC和AUPRC方面的性能.

主要方法:

对3931名手术患者进行前性队列研究.
开发了天真贝叶斯模型,密集的神经网络和基于规则的模型.
使用灵敏度,工作量减少,AUROC和AUPRC在0.5决策值的模型性能评估.

主要成果:

最好的ML模型实现了高达0.90的灵敏度,高达0.968的AUROC,高达0.248的AUPRC,以及超过90%的工作负载减少.
基于规则的模型显示了更高的灵敏度 (0.954),但更低的AUROC,AUPRC和工作量减少.
半自动化方法证明了有效SSI检测的潜力.

结论:

半自动化ML和基于规则的模型可以支持高效和准确的SSI监控.
这些方法显著减少了与传统监控相关的手工工作量.
建议在不同的医疗保健机构进一步验证.

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