Abstract
There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model.
Highlights
People consult medical experts online in healthcare communities and ask for treatment plans through symptom description or seek the recommended hospital and department
Our primary contribution is a new Double Channel Long Short-Term Memory (LSTM) model, called DC-LSTM, and we add a hybrid attention mechanism to LSTM, which can selectively learn long sequences and make deep neural networks in each batch of training. is proposed model can learn different forms of features, enhance model learning and expression skills, and prevent overfitting. e experimental results show that the DC-LSTM model can significantly improve the accuracy compared with other Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) models
Some words contribute more to the modality, some words have less contribution to the modality, and the emotions expressed by each word are prioritized. erefore, in order to solve the problem of not being able to selectively learn the emotional characteristics of each word, we can add hybrid attention after LSTM and make trade-offs for different words with different emotions, improve the learning ability of LSTM model, and improve the special characteristics of neural network learning. is creates the ability to simultaneously improve generalization and prevent overfitting from occurring. e model is divided into three parts of CNN-LSTM, Double Channel, and hybrid attention, of which Double Channel is the most important structure
Summary
Using a deep learning algorithm to classify disease symptom text can optimize the allocation of medical resources and improve the efficiency of medical treatment. For an effective medical diagnosis, we proposed the idea of using an improved LSTM model to implement medical consultation text classification. Some models of Chinese question and answer in the medical field have been proposed. Yin et al [5] designed an algorithm for clustering and similarity evaluation of similar questions and answers for the problem of low efficiency of online healthcare consultation. Zhang et al [7] proposed an endto-end word embedding multiscale CNN model for question and answer matching in the medical field. In the medical diagnosis classification, using this model can help people quickly choose the right outpatient department for medical treatment and improve the efficiency of outpatient service
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