模块化临床决策支持网络 (MoDN) - 可更新,可解释和可移植的预测,用于不断变化的临床环境
Cécile Trottet1, Thijs Vogels1, Kristina Keitel2
1Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
PLOS digital health
|July 17, 2023
概括
模块化临床决策支持网络 (MODN) 能够在不完全互操作的临床决策支持系统中实现保护隐私的协作学习. 这种新的方法提高了诊断准确度,并为临床医生提供可解释的反.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 机器学习 机器学习
背景情况:
- 临床决策支持系统 (CDSS) 通常使用静态逻辑,导致不一致的性能.
- 数据驱动型号需要大量的数据集,这给互操作性和数据共享带来了挑战.
- 现有系统在不完美互操作 (IIO) 或不可共享的数据集上扎.
研究的目的:
- 引入模块化临床决策支持网络 (MoDN) 以在IIO CDSS数据集中实现灵活,保护隐私的学习.
- 解决CDSS衍生数据中的系统缺失问题,并提供可解释的,持续的反.
- 为了使协作学习没有直接的数据共享.
主要方法:
- 开发了MoDN,一种使用特征特定神经网络模块的新决策树.
- 在坦桑尼亚3,192名儿科门诊患者的现实CDSS数据集上验证了MODN.
- 通过创建不同特征重叠的子集和移植 MoDN 模型来测试协作学习.
主要成果:
- 莫迪N显著优于单一基线模型 (F1得分为0.749与物流回归为0.651,MLP为0.620).
- 使用MoDN的协作学习实现了近乎理想的性能,即使数据集之间只有60%的特征重叠.
- MODN提供了可解释的,连续的预测反,集成到咨询逻辑中.
结论:
- 摩登提供了一个强大的解决方案,用于保护隐私,跨IIO CDSS的协作学习.
- 模块化设计提高了在不断变化的临床环境中的适应性和性能.
- 通过可解释的实时反,MODN提高了诊断准确性和临床医生的支持.
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