Traditional Chinese Medicine Information Analysis Based on Multi-task Joint Learning Model

Abstract

In the field of traditional Chinese medicine (TCM) informatics, Chinese word segmentation and syndrome differentiation are two crucial analysis tasks. Owing to the ambiguity of the Chinese language and the peculiarities of syndrome differentiation, these tasks face huge challenges. Notably, from previous studies and investigations, these two tasks have a high correlation, which makes them fit the idea of multi-task joint learning (MTL). By sharing the underlying parameters and adding two different task loss functions, we proposed a novel MTL method to perform segmentation and classification of medical records in this research. Moreover, two classic deep neural network (Bidirectional LSTM (Bi-LSTM) and TextCNN) are fused into the MTL to conduct these two tasks simultaneously. As far as we know, our approach is the first attempt to combine these tasks with the idea of MTL. We used our proposed method to conduct a large number of comparative experiments. Through experimental comparison, it can be proved that our method is superior to other methods on both tasks. Therefore, this research can help to realize the modernization of TCM and the intelligent differentiation of TCM.