A Disease Interaction Retrieval and Recommendation System based on NLP Technology

Abstract

There is a great request for a Chatbot system that uses natural language processing for disease retrieval and recommendation in medical treatment with interactive functions. This paper uses cMedQA2 data for training, developing and testing. The Chinese word segmentation tool jieba to reassemble the text data of consecutive words into the word sequences, and uses Bag of Words to count the frequency of keywords appearing in sentences, and uses N-gram method to predict whether the evaluation and prediction of a sentence is reasonable. This paper proposes an improved TextCNN network framework structure, which uses overlap technology to ensure the contextual coherence of information. The final experimental results show that there are 96.2% on the training dataset and 87.5% on the testing dataset.