在医院环境中识别和预测多药耐药生物的进口和无症状传播者
Jiaming Cui1,2, Jack Heavey3, Eili Klein4,5,6
1College of Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
一个新的NeurABM框架通过结合神经网络和基于代理物的模型,准确地识别出来自多药耐药生物体 (MDROs) 的医疗保健相关感染 (HAI),从而改善患者护理和预测医院感染.
科学领域:
- 计算流行病学计算流行病学
- 传染病建模传染病模型
- 机器学习在医疗保健中的应用
背景情况:
- 由多药耐药生物体 (MDROs) 引起的与医疗保健相关的感染 (HAI) 是医疗保健面临的一个重大挑战.
- 由于无症状病例和测试限制,很难区分进口和医院感染.
研究的目的:
- 开发一个新的框架,NeurABM,整合神经网络和基于代理的模型 (ABM).
- 改善患者输入感染的识别和医院感染的预测.
主要方法:
- 开发了NeurABM,一种混合模型,将神经网络用于患者级预测和ABM用于传输动态.
- 在综合框架内利用电子健康记录 (EHR) 数据.
- 同时学习进口预测和感染识别模型.
主要成果:
- 在识别进口案件方面,NeurABM显著优于现有的方法.
- 该框架在预测未来的医院感染方面表现出卓越的准确性.
- 成功地利用了EHR数据和机械洞察力,以改善预测.
结论:
- 神经ABM代表了准确识别感染进口和预测医院感染的突破.
- 综合方法克服了纯粹传输或机器学习模型的局限性.
- 为临床实践提供了一个强大的工具来管理MDRO的传播.
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