Discovering Symptom-herb Relationship by Exploiting SHT Topic Model

TCM has been widely researched through various methods in computer science in past decades, but none digs into huge amount of clinical cases to discover the meaningful treatment patterns between symptoms and herbs. To meet the challenge, we explore the unstructured and intricate experiential data in clinical case, and propose a method to discover the treatment patterns by introducing a novel topic model named SHT (Symptom-Herb Topic model). Combinational rules are incorporated into the learning process. We evaluate our method on 3,765 TCM clinical cases. The experiment validates the effectiveness of our method compared with LDA model and LinkLDA model.