Health Care Spoken Dialogue System for Diagnostic Reasoning and Medical Product Recommendation

Abstract

We proposed a medical dialogue system based on word embedding and slot filling. After the input speech is converted into text through ASR, the sentences are cut into words through the Jieba word segmentation system. We vectorized the words of the input sentence with the word embedding model, extract information with slot filling based on the cosine similarity, and then the diagnostic reasoning simulation is performed. We adopt the concept of TF-IDF algorithm to train the weight of the symptoms and diseases in our medical database. The more common the disease, the higher the weight. As for symptom, the more the disease has this symptom, the lower the weight of the symptom. After knowing the weight of disease and symptom, we can start to calculate the score of disease and get the most likely disease. Finally, the most suitable product is returned to the user. In the experimental result, the accuracy of slot filling and diagnostic reasoning simulation was 88% and 86% respectively.