Using recurrent neural network models for early detection of heart failure onset.
Using recurrent neural network models for early detection of heart failure onset.
复制标题
DOI：
10.1093/jamia/ocw112
复制
发表时间：
2017-03-01
期刊：
影响因子：
--
通讯作者：
Sun J
中科院分区：
文献类型：
--
作者：
Choi E;Schuetz A;Stewart WF;Sun J
目的：与忽略时间性的传统方法相比，我们探讨了使用深度学习对电子健康记录(EHR)中事件之间的时间关系进行建模是否会改善预测心力衰竭(HF)初始诊断的模型性能。材料和方法：数据来自卫生系统的电子病历，在2000年5月16日至2013年5月23日期间，3,884例心力衰竭病例和28名 903对照被确认为初级保健患者。使用门控递归单元(GRU)的递归神经网络(RNN)模型被用于检测时间戳事件(例如，疾病诊断、用药顺序、手术顺序等)之间的关系。有12至18个月的病例和对照观察窗口。模型性能指标与正则化Logistic回归、神经网络、支持向量机和K近邻分类器方法进行了比较。结果：在12个月的观察窗口中，神经网络模型的曲线下面积为0.777，而Logistic回归模型的曲线下面积为0.747，1隐含层的多层感知器模型为0.765，支持向量机模型为0.743，K近邻模型为0.730。当使用18个月的观察窗口时，RNN模型的AUC值增加到0.883，并且显著高于最佳基线方法的AUC值0.834。结论：适应于利用时间关系的深度学习模型似乎改善了在12-18个月的短观察窗口内检测突发心力衰竭的模型的性能。
Objective: We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality. Materials and Methods: Data were from a health system’s EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12- to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches. Results: Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP). Conclusion: Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12–18 months.