A hierarchical symptom-herb topic model for analyzing traditional Chinese medicine clinical diabetic data

Abstract

Traditional Chinese medicine (TCM) is a clinical medicine. The huge clinical data from the daily clinical process which keeps to TCM theories and principles, is the core empirical knowledge source for TCM research. Induction of the common knowledge or regularities from the large-scale clinical data is a vital task for both theoretical and clinical research of TCM. Topic model have recently shown much success for text analysis and information retrieval by extracting latent topics from text collection. In this paper, we propose a hierarchical symptomherb topic model (HSHT), to automatically extract the hierarchical latent topic structures with both symptoms and their corresponding herbs in the TCM clinical data. The HSHT model is one of the extensions of hierarchical latent Dirichlet allocation model (hLDA) and Link latent Dirichlet allocation (LinkLDA). The proposed HSHT model is used for extracting the hierarchical structure of symptoms with their corresponding herbs in clinical type 2 diabetes mellitus (T2DM). We get one meaningful supertopic with common symptoms and commonly used herbs and some meaningful subtopics denoted T2DM complications with corresponding symptoms and their commonly used herbs. The results indicate some important medical groups corresponding to the companioned diseases in the T2DM inpatients. And then the results show that there exactly exist TCM diagnosis and treatment sub-categories and the personalized therapies to T2DM. Furthermore, it manifested that the HSHT model is useful for establishing of the TCM clinical guidelines based on the TCM clinical data.

Discovering Symptom-herb Relationship by Exploiting SHT Topic Model

“…Yao et al [11] employed Labeled-LDA (Labeled Latent Dirichlet Allocation) to mine treatment patterns in TCM clinical cases, but it only discovered the treatment patterns between herbs and disease by supervised model, which required labeled training data. The main goal of our paper is close to Zhang et al [8]. However, we are different from theirs because: 1) We propose separate modeling for symptoms and herbs; 2) combinational rules between herbs are incorporated into the process of topic modeling, which is more consistent with TCM theory; that is, when two herbs are used together, their interaction should display their superiority over a single herb in the treatment of diseases.…”

“…Lin et al [3] proposed a symptom-herb-therapies-diagnosis topic model to diagnose the disease and administer appropriate drugs and treatments given a patient's symptoms. Zhang et al [8] proposed a hierarchical topic model (HSHT) to automatically extract the hierarchical latent topic structures with both symptoms and their corresponding herbs in the TCM clinical data. Yao et al [11] employed Labeled-LDA (Labeled Latent Dirichlet Allocation) to mine treatment patterns in TCM clinical cases, but it only discovered the treatment patterns between herbs and disease by supervised model, which required labeled training data.…”

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

“…Zhang et al [ 129 ] applied a hierarchical symptom-herb topic (HSHT) model to analyze clinical diabetic data. They constructed a hierarchical symptom-herb topic model to describe the latent structures with both symptoms and their corresponding herbs.…”

Data processing and analysis in real‐world traditional Chinese medicine clinical data: challenges and approaches

“…Zhang et al . had proposed a hierarchical author–topic model to find the multilevel symptom–herb associations in 3238 inpatient cases with type 2 diabetes. Using Gibbs sampling algorithm and latent Dirichlet allocation method , the model could extract clinically meaningful hierarchical topics including both symptoms and herbs from the TCM clinical data set.…”