1 research outputs found
Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models
Traditional Chinese Medicine (TCM) has accumulated a big amount of precious
resource in the long history of development. TCM prescriptions that consist of
TCM herbs are an important form of TCM treatment, which are similar to natural
language documents, but in a weakly ordered fashion. Directly adapting language
modeling style methods to learn the embeddings of the herbs can be problematic
as the herbs are not strictly in order, the herbs in the front of the
prescription can be connected to the very last ones. In this paper, we propose
to represent TCM herbs with distributed representations via Prescription Level
Language Modeling (PLLM). In one of our experiments, the correlation between
our calculated similarity between medicines and the judgment of professionals
achieves a Spearman score of 55.35 indicating a strong correlation, which
surpasses human beginners (TCM related field bachelor student) by a big margin
(over 10%)