在低中等收入国家 (LMICs) 合成电子健康记录用于预测模型
Ghadeer O Ghosheh1, C Louise Thwaites2,3, Tingting Zhu1
1Department of Engineering Sciences, University of Oxford, Oxford OX1 3PJ, UK.
Biomedicines
|June 28, 2023
概括
深度生成模型创建合成电子健康记录 (EHR),以克服低资源环境中的数据限制. 在这种合成数据上训练的模型有效地预测了医院获得的感染,优于传统方法.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于临床决策支持
- 低中等收入国家 (LMICs) 的健康信息学
背景情况:
- 机器学习 (ML) 和电子健康记录 (EHR) 的普及促进了临床决策支持系统.
- 数据的限制,包括中低收入国家 (LMICs) 的小规模和不规则的抽样,阻碍了ML在医疗保健中的应用.
- 深度生成模型 (DGM) 通过创建现实的合成EHR数据来提供解决方案,从而保护患者的隐私.
研究的目的:
- 从一个小的LMIC数据集中使用DGM生成合成EHR数据.
- 使用这些合成数据开发和评估ML模型来预测医院获得的感染.
- 评估合成数据大小对模型性能和可解释性的影响.
主要方法:
- 利用深度生成模型从LMIC的364名患者的初始数据集中合成EHR数据.
- 在生成的合成数据上训练有素的诊断模型,以使用最小的ICU入院数据预测医院获得的感染.
- 用合成数据训练的模型的性能与用原始数据和SMOTE超样本数据训练的模型的性能进行了比较.
主要成果:
- 在合成数据上训练的诊断模型与在原始或过量采样数据上训练的模型相比,表现优越.
- 改变合成数据大小的实验显示了其对模型性能和可解释性的影响.
- 深度生成模型显示出对开发和验证医疗保健模型的重大承诺,即使原始数据有限.
结论:
- 深度生成模型在克服LMICs数据稀缺性挑战方面是有效的,用于开发临床预测模型.
- 由DGMs生成的合成EHR数据可以使医疗保健提供者能够构建和验证基本的数据驱动工具.
- 这种方法有可能在资源有限的环境中推进医疗分析和决策支持.
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