论文标题
模块化临床决策支持网络（MODN） - 不断发展的临床环境的可更新，可解释和便携式预测
Modular Clinical Decision Support Networks (MoDN) -- Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments
论文作者
论文摘要
数据驱动的临床决策支持系统（CDSS）有可能通过个性化的概率指导来改善和标准化护理。但是，所需的数据大小需要从类似的CDSS中进行协作学习，而CDSS通常是不可出现的或不完美的可互操作（IIO），这意味着它们的功能集并非完全重叠。我们提出了模块化临床决策网络（MODN），该网络允许在IIO数据集中灵活，保护隐私的学习，同时为临床医生提供可解释的，连续的预测反馈。 MODN是由特定特定的神经网络模块组成的新颖决策树。它创建了患者的动态个性化表示，并且可以在咨询的每个步骤中对诊断进行多个预测。模块化设计使其可以将培训更新到特定功能，并在IIO数据集之间进行协作，而无需共享任何数据。
Data-driven Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with personalised probabilistic guidance. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often unsharable or imperfectly interoperable (IIO), meaning their feature sets are not perfectly overlapping. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules. It creates dynamic personalised representations of patients, and can make multiple predictions of diagnoses, updatable at each step of a consultation. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data.
