医院感染监测:机器学习算法反映了国家医疗保健安全网络的定义
Stephani Amanda Lukasewicz Ferreira1, Arateus Crysham Franco Meneses1, Tiago Andres Vaz1
1Qualis, Porto Alegre, Rio Grande do Sul, Brazil.
Infection control and hospital epidemiology
|January 11, 2024
概括
机器学习通过识别更多病例,特别是呼吸道感染,显著改善了医院获得感染 (HAI) 的检测. 这种人工智能工具增强了传统的监视方法,以更好地控制感染.
科学领域:
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 医院获得感染 (HAI) 的监测对于感染控制至关重要.
- 机器学习 (ML) 显示出增强HAI监控的希望.
- 本研究将手动监控与基于ML的半自动化方法进行比较.
研究的目的:
- 为了比较手动HAI监控与监督,半自动ML方法的有效性.
- 探索ML模型识别的感染类型和重要特征.
- 评估ML在提高HAI检测率方面的潜力.
主要方法:
- 一个半自动化的ML随机森林算法于2021年7月至12月在巴西一家医院实施.
- 住院病人的病历被手动和ML方法检查.
- 一个专家小组验证了ML识别的HAI.
主要成果:
- 半自动化方法确定4.7%的患者有HAI,而手动监测的患者为2.9%.
- 通过ML方法检测到77种额外的呼吸道感染,占新发现的HAI的93.9%.
- ML模型使用了447个特征,超过50%与HAI分类NHSN标准保持一致.
结论:
- 机器学习算法增强了人类HAI分类能力,比传统方法识别更多病例.
- 包含许多功能,包括NHSN标准,提高了该模型的性能.
- 记录的ML算法性能可以支持将AI整合到临床和流行病学实践中,以改善HAI监测.
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