论文标题
用于多模式临床数据分析的动态深神经网络
A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis
论文作者
论文摘要
来自电子病历，注册表或试验的临床数据提供了应用机器学习方法的大量信息来源，以培养Precision Medicine，例如通过找到新的疾病表型或进行个体疾病预测。但是，为了充分利用临床数据的深度学习方法，必须进行架构1）在缺失和错误的价值方面具有牢固性，而2）可以处理高度可变的尺寸列表以及个人诊断，程序，测量和药物处方的长期依赖性。在这项工作中，我们详细阐述了在这种情况下对完全连接的神经网络和经典的机器学习方法的局限性，并提出了一种新型的经常性神经网络体系结构Apaptivenet，可以处理多个不同事件的列表，从而减轻上述局限性。我们使用瑞士临床质量管理注册中心使用该结构来解决类风湿关节炎的疾病进展预测问题，该注册中心包含超过10.000名患者和65.000多个患者就诊。我们提出的方法会导致更紧凑的表示，并胜过经典基线。
Clinical data from electronic medical records, registries or trials provide a large source of information to apply machine learning methods in order to foster precision medicine, e.g. by finding new disease phenotypes or performing individual disease prediction. However, to take full advantage of deep learning methods on clinical data, architectures are necessary that 1) are robust with respect to missing and wrong values, and 2) can deal with highly variable-sized lists and long-term dependencies of individual diagnosis, procedures, measurements and medication prescriptions. In this work, we elaborate limitations of fully-connected neural networks and classical machine learning methods in this context and propose AdaptiveNet, a novel recurrent neural network architecture, which can deal with multiple lists of different events, alleviating the aforementioned limitations. We employ the architecture to the problem of disease progression prediction in rheumatoid arthritis using the Swiss Clinical Quality Management registry, which contains over 10.000 patients and more than 65.000 patient visits. Our proposed approach leads to more compact representations and outperforms the classical baselines.
