%0 Journal Article
%T 基于电子病历的疾病风险预测<br/>Disease Risk Prediction Based on EHR
%A 李宁宁
%A 陆迁
%A 梁敏
%A 林栋
%A 莫毓昌
%J Hans Journal of Data Mining
%P 47-55
%@ 2163-1468
%D 2020
%I Hans Publishing
%R 10.12677/HJDM.2020.101005
%X 数据驱动的医疗保健(data driven healthcare)作为可用的大型医疗数据的使用，以提供最好的和最个性化的护理，正成为医疗行业革命成功的主要趋势之一。电子病历是推动这一数据驱动的医疗革命成功的主要载体。本文运用深度学习的方法，基于词嵌入模型实现对电子病历信息的表示，利用长短期记忆(LSTM)网络模型特性，实现对电子病历信息时间上的无规律性和疾病信息长期依赖挑战的解决，实现对疾病风险的预测，通过与卷积神经网络(CNN)模型进行比对，实验结果显示本文方法的有效性。<br />
Data driven health care, as the use of available large-scale medical data, to provide the best and most personalized care, is becoming one of the main trends of the success of the revolution in the medical industry. Electronic health record is the main carrier to promote the success of this da-ta-driven medical revolution. In this paper, we use the method of deep learning, based on the word embedding model to express the EHR information, and use the characteristics of the long-term memory network model to solve the irregular time of EHR information and the long-term depend-ence of disease information, so as to achieve the prediction of disease risk. Compared with the con-volution neural network model, the results show the effectiveness of this method.
%K 深度学习，电子病历，词嵌入，长短期记忆网络<br/>Deep Learning
%K Electronic Health Records
%K Word Embedding
%K Long Short-Term Memory Network
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=33876