Predicting the Risk of Heart Failure With EHR Sequential Data Modeling
Predicting the Risk of Heart Failure With EHR Sequential Data Modeling
复制标题
通过 EHR 序列数据建模预测心力衰竭的风险
DOI：
10.1109/access.2017.2789324
复制
发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Wei, Xiaopeng
中科院分区：
文献类型：
--
作者：
Jin, Bo;Che, Chao;Wei, Xiaopeng
电子健康记录(EHR)包含患者诊断记录、医生记录和医院科室记录。对于心力衰竭，我们可以从EHR时间序列中获得大量的非结构化数据。通过分析和挖掘这些基于时间的EHR，我们可以识别诊断事件之间的联系，并最终预测患者将被诊断的时间。然而，现有的电子病历数据由于稀疏性和非标准化，很难直接使用。因此，本文提出了一种有效且健壮的心力衰竭预测体系结构。本文的主要贡献是使用神经网络预测心力衰竭(即根据患者的电子医疗数据预测心脏疾病的可能性)。具体地说，我们使用One-Hot码和单词向量来模拟诊断事件，并使用长短期记忆网络模型的基本原理来预测心力衰竭事件。基于真实世界数据集的评估表明，所提出的体系结构在预测心力衰竭风险方面具有良好的实用性和有效性。
Electronic health records (EHRs) contain patient diagnostic records, physician records, and records of hospital departments. For heart failure, we can obtain mass unstructured data from EHR time series. By analyzing and mining these time-based EHRs, we can identify the links between diagnostic events and ultimately predict when a patient will be diagnosed. However, it is difficult to use the existing EHR data directly, because they are sparse and non-standardized. Thus, this paper proposes an effective and robust architecture for heart failure prediction. The main contribution of this paper is to predict heart failure using a neural network (i.e., to predict the possibility of cardiac illness based on patient’s electronic medical data). Specifically, we employed one-hot encoding and word vectors to model the diagnosis events and predicted heart failure events using the basic principles of a long short-term memory network model. Evaluations based on a real-world data set demonstrate the promising utility and efficacy of the proposed architecture in the prediction of the risk of heart failure.