Discovering treatment pattern in Traditional Chinese Medicine clinical cases by exploiting supervised topic model and domain knowledge

Zhang

Experimental and Therapeutic Medicine

et al. 2016

Description of syndromes and symptoms in traditional Chinese medicine are extremely complicated. The method utilized to diagnose a patient's syndrome more efficiently is the primary aim of clinical health care workers. In the present study, two models were presented concerning this issue. The first is the latent semantic analysis (LSA)-based semantic classification model, which is employed when the classification and words used to depict these classfications have been confirmed. The second is the symptom-herb-therapies-diagnosis topic (SHTDT), which is employed when the classification has not been confirmed or described. The experimental results showed that this method was successful, and symptoms can be diagnosed to a certain extent. The experimental results indicated that the topic feature reflected patient characteristics and the topic structure was obtained, which was clinically significant. The experimental results showed that when provided with a patient's symptoms, the model can be used to predict the theme and diagnose the disease, and administer appropriate drugs and treatments. Additionally, the SHTDT model prediction results did not yield completely accurate results because this prediction is equivalent to multi-label prediction, whereby the drugs, treatment and diagnosis are considered as labels. In conclusion, diagnosis, and the drug and treatment administered are based on human factors.

Section: Resultsmentioning

confidence: 99%

Study of TCM clinical records based on LSA and LDA SHTDT model

Zhang

Experimental and Therapeutic Medicine

et al. 2016

Computational and Mathematical Methods in Medicine

“…The former technology attempts to recognize the correct pathological information such as pulse condition [9][10][11][12][13][14] and tongue diagnosis [15] of an individual patient. Whereas the later one, knowledge mining, mainly focuses on finding out various kinds of hidden relationships in the knowledge, for example, the relationships between symptom and symptom, symptom and syndrome, and, syndrome and disease [16][17][18][19][20]. In addition, it should be noted that there are many other studies that deserve attention as well, such as classifying herbs by convolutional neural network model [21], using deep learning mode to explore the relationship between herbal property and action [22].…”

Section: Introductionmentioning

confidence: 99%

An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example

Yao

Wang

et al. 2019

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.

Concurrency and Computation

“…Data mining technologies have been paid extensive attention by academic researchers and clinicians . Currently, more researchers use data mining technologies to study Chinese medicine . Zhou et al proposed a new data warehouse system, which stored various information entities and clinical relationships from the clinical practices in TCM.…”

Section: Introductionmentioning

confidence: 99%

“…Kang et al integrated the biomedical indexes in modern medicine into the four‐diagnostic information of TCM and proved that the integrating method obtained better performance on 634 patient cases from health controls and three subtypes of Chronic Hepatitis B syndromes. Yao et al introduced a framework that can mine treatment patterns in TCM clinical cases.…”

Section: Introductionmentioning

confidence: 99%

Multi‐label feature selection with application to TCM state identification

Dai

Zhang

et al. 2018

Summary The goal of TCM state identification is to identify the patient's syndromes and locations and natures of diseases according to symptoms. Generally, symptoms of a patient are associated with several syndromes and multiple locations and natures of diseases; hence, the TCM state identification is a typical multi‐label problem. In this paper, a new method is proposed to predict syndromes and locations and natures of diseases according to the diagnostic information of TCM. In detail, the correlation between features and the correlation between class labels are combined into a new uniform feature space. After that, the MDMR algorithm is used to select the most discriminatory features from the new uniform feature space, which is helpful to reduce the data dimensionality. Lastly, a KNN‐like algorithm is modified to calculate the label similarity of test data, and the finite set of labels of test data is predicted by ML‐KNN. In this paper, the test data is collected by Fujian University of Traditional Chinese Medicine according to the theory of TCM and medical ethics. The experiments show that the performance of the proposed method is superior to some other popular methods and is helpful in the identification of health state in TCM.