Reduce the medical burden: An automatic medical triage system using text classification BERT based on Transformer structure

To reduce the pressure of medical triage in the hospitals, this paper proposes a medical triage system that could classify patients' questions or texts about their symptoms into several given categories to give suggestions on which kind of consulting room patients could choose. First, we have done extensive research on the medical care situation and the hospitals' problems in China and conclude that reducing the triage pressure is of great importance for hospitals. We then collect the medical Question Answering datasets, including questions and answers with symptom tags. According to the form of our data, we use BERT, a mainstream model in Natural Language Processing, as the base of our system and modify it with additional components specified to our task. We developed two models based on different datasets. One is trained by data from the five most frequent symptom tags. And for the other one, we use the whole dataset by identifying the appearance of special words, measuring the overlap of all the tags, and merging them into 20 categories. Both of them utilize several training techniques and result in relatively high accuracy: top1 85% accuracy and top2 accuracy 96% on the smaller dataset, top1 accuracy 66.2%, and top2 accuracy 78.3% on the other one. Then we analyze the results and build up our web system for medical use. If given real-world data with similar data distribution, our system could help patients judge diseases and alleviate the triage problem in medical treatment to a certain extent. Moreover, a similar strategy of our model could also be adapted for use in different fields like book searching in libraries. Therefore, our system has a broad application prospect.