Herb-Know: Knowledge Enhanced Prescription Generation for Traditional Chinese Medicine

Prescription generation of traditional Chinese medicine (TCM) is a meaningful and challenging problem. Previous researches mainly model the relationship between symptoms and herbal prescription directly. However, TCM practitioners often take herb effects into consideration when prescribing. Few works focus on fusing the external knowledge of herbs. In this paper, we explore how to generate a prescription with the knowledge of herb effects under the given symptoms. We propose Herb-Know, a sequence to sequence (seq2seq) model with pointer network, where the prescription is conditioned over two inputs (symptoms and pre-selected herb candidates). To the best of our knowledge, this is the first attempt to generate a prescription with a knowledge enhanced seq2seq model. The experimental results demonstrate that our method can make use of knowledge to generate informative and reasonable herbs, which outperforms other baseline models.