基于Word2Vec与LSTM病历文本分类研究

Research on text classification of medical records based on Word2Vec and LSTM

医疗系统的不断完善,产生了大量的医疗电子病历文本数据,其中含有数量可观的有借鉴性的医疗文本信息,而在对电子病历文本挖掘和利用方面,一直存在有效信息分类难度大、利用率低等问题。为了解决以上问题,设计一种基于Word2Vec与长短期记忆神经网络(LSTM)的文本分类模型,使用Word2Vec模型计算文本向量作为LSTM的输入,构建两组对照模型,分别是基于支持向量机(SVM)的文本分类算法以及基于卷积神经网络(CNN)的文本分类算法,实验结果显示该方法在分类效果上优于其它对照模型,具有一定的实用价值。

With the continuous improvement of the medical system,a large number of medical electronic medical record text data are generated.It contains a considerable amount of reference medical text information,in terms of text mining and utilization of electronic medical records.There have always been problems of difficult classification and low utilization of effective information.In order to solve the above problems,this paper attempts to propose a text classification model based on Word2Vec and long short‑term memory neural network(LSTM)and the text vector is calculated by Word2Vec model as the input of LSTM.This paper constructs two groups of control models,they are text classification algorithm based on support vector machine(SVM)and text classification algorithm based on convolutional neural network(CNN).The experimental results show that this method is superior to other control models in classification effect,and has a certain practical value.