Guo-Ping Liu

Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning

BackgroundCoronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.MethodsStandardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.ResultsA total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.ConclusionsThe novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.

Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine

In traditional Chinese medicine (TCM) diagnosis, a patient may be associated with more than one syndrome tags, and its computer-aided diagnosis is a typical application in the domain of multi-label learning of high-dimensional data. It is common that a great deal of symptoms can occur in traditional Chinese medical diagnosis, which affects the modeling of diagnostic algorithm. Feature selection entails choosing the smallest feature subset of relevant symptoms, and maximizing the generalization performance of the model. At present there are rare researches on feature selection on multi-label data. A hybrid optimization technique is introduced to symptom selection for multi-label data in TCM diagnosis in this paper, and modeling is made by means of four multi-label learning algorithms like k nearest neighbors, etc. We compare the performance of the algorithm with the current popular dimension reduction algorithms like MEFS (embedded feature selection for multi-Label learning), MDDM (multi-label dimensionality reduction via dependence maximization) on the UCI Yeast gene functional data set and an inquiry diagnosis dataset of coronary heart disease (CHD). Experimental results show that the algorithm we present has significantly improved the performance. In particular, the improvement on the average precision for the classifier is up to 10.62% and 14.54%. Syndrome inquiry modeling of CHD in TCM is realized in this paper, providing effective reference for the diagnosis of CHD and analysis of other multi-label data.

Inquiry diagnosis of coronary heart disease in Chinese medicine based on symptom-syndrome interactions

BackgroundThere is a long history of coronary heart disease (CHD) diagnosis and treatment in Chinese medicine (CM), but a formalized description of CM knowledge is still unavailable. This study aims to analyze a set of CM clinical data, which is important and urgent.MethodsRelative associated density (RAD) was used to analyze the one-way links between the symptoms or syndromes or both. RAD results were further used in symptom selection.ResultsAnalysis of a dataset of clinical CHD diagnosis revealed some significant relationships, not only between syndromes but also between symptoms and syndromes. Using RAD to select symptoms based on different classifiers improved the accuracy of syndrome prediction. Compared with other traditional symptom selection methods, RAD provided a higher interpretability of the CM data.ConclusionThe RAD method is effective for CM clinical data analysis, particular for analysis of relationships between symptoms in diagnosis and generation of compact and comprehensible symptom feature subsets.

Application of multilabel learning using the relevant feature for each label in chronic gastritis syndrome diagnosis.

Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

Study on intelligent syndrome differentiation in Traditional Chinese Medicine based on multiple information fusion methods

Numerous researchers have taken the solid step forward towards the objectification research of Traditional Chinese Medicine (TCM) four diagnostic methods. However, it is deficient in studies on information fusion of the four diagnostic methods. We establish four-diagnosis syndrome differentiation model of TCM based on information fusion technology. The objective detection instruments of four-diagnostic method are applied to collect four-diagnosis objective information of 506 cases of clinical heart-system patients. Then multiple information fusion methods are adopted to establish recognition model of syndromes. The results of our experiments show that recognition rates of the six syndromes using multi-label learning is better than OCON artificial neural network and multiple support vector machine.

Deep learning based syndrome diagnosis of chronic gastritis.

In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

Analysis of the diagnostic consistency of Chinese medicine specialists in cardiovascular disease cases and syndrome identification based on the relevant feature for each label learning method

ObjectiveTo analyze the diagnostic consistency of Chinese medicine (CM) specialists in patients with cardiovascular disease and to study syndrome classification and identification based on the multi-label learning method.MethodsUsing self-developed CM clinical scales to collect cases, inquiry information, complexity, tongue manifestation and pulse manifestation were assessed. The number of cases collected was 2,218. Firstly, each case was differentiated by two CM specialists according to the same diagnostic criteria. The consistency of the diagnosis based on Cohen’s Kappa coefficient was analyzed. Secondly, take the same diagnosis syndromes of two specialists as the results of the cases. According to injury information in the CM scale “yes” or “no” was assigned “1” or “0”, and according to the syndrome type in each case “yes” or “no” was assigned “1” or “0”. CM information data on cardiovascular disease cases were established. We studied CM syndrome classification and identification based on the relevant feature for each label (REAL) learning method, and the diagnostic rate of the syndrome was studied using the REAL method when the number of features selected was 5, 10, 15, 20, 30, 50, 70, and 100, respectively.ResultsThe syndromes with good diagnostic consistency were Heart (Xin)-qi deficiency, Heart-yang deficiency, Heart-yin deficiency, phlegm, stagnation of blood and stagnation of qi. Syndromes with poor diagnostic consistency were heartblood deficiency and blood deficiency of Heart and Liver (Gan). The highest diagnostic rates using the REAL method were Heart-yang deficiency followed by Heart-qi deficiency. A different number of features, such as 5, 10, 15, 20, 30, 40, 50, 70, and 100, respectively, were selected and the diagnostic accuracy based on five features showed the highest diagnostic accuracy. The top five features which had a strong correlation with the syndromes were in accordance with the CM theory.ConclusionsCM syndrome differentiation is strongly subjective and it is difficult to obtain good diagnostic consistency. The REAL method fully considers the relationship between syndrome types and injury symptoms, and is suitable for the establishment of models for CM syndrome classification and identification. This method can probably provide the prerequisite for objectivity and standardization of CM differentiation.

Analysis and Recognition of Traditional Chinese Medicine Pulse Based on the Hilbert-Huang Transform and Random Forest in Patients with Coronary Heart Disease

Objective. This research provides objective and quantitative parameters of the traditional Chinese medicine (TCM) pulse conditions for distinguishing between patients with the coronary heart disease (CHD) and normal people by using the proposed classification approach based on Hilbert-Huang transform (HHT) and random forest. Methods. The energy and the sample entropy features were extracted by applying the HHT to TCM pulse by treating these pulse signals as time series. By using the random forest classifier, the extracted two types of features and their combination were, respectively, used as input data to establish classification model. Results. Statistical results showed that there were significant differences in the pulse energy and sample entropy between the CHD group and the normal group. Moreover, the energy features, sample entropy features, and their combination were inputted as pulse feature vectors; the corresponding average recognition rates were 84%, 76.35%, and 90.21%, respectively. Conclusion. The proposed approach could be appropriately used to analyze pulses of patients with CHD, which can lay a foundation for research on objective and quantitative criteria on disease diagnosis or Zheng differentiation.

Association analysis and distribution of chronic gastritis syndromes based on associated density

Purpose: The analysis of syndrome distribution and the association between syndrome-syndrome in chronic gastritis (CG) patients can provide references for research about Traditional Chinese Medicine (TCM) diagnosis and treatment of CG. Method: This paper applies the investigation method of clinical epidemiology, adopts probability statistics method and comes up with the concept of associated density to conduct association analysis of syndromes. Result: In the distribution of syndromes patients who have a single syndrome occupy 64.7% of the whole sample; patients who have two syndromes make up 32.2%; the situation that 3 syndromes happen at the same time has a percentage of 1.3%.spleen-stomach qi deficiency syndrome and liver qi stagnation syndrome are closely associated. Conclusion: Liver and spleen are the main disease locations of CG and these two organs are associated with and influenced each other in physiology and pathology.

Symptom selection of inquiry diagnosis data for coronary heart disease in Traditional Chinese Medicine by using social network techniques

Coronary heart disease (CHD) is a common cardiovascular disease in the elderly, which causes high death rate and low cure rate. Therefore, the TCM diagnosis objectification is important. This paper proposes the Relative Associated Density (RAD) method to analyze the data set. RAD results of the symptoms to syndromes are used in performing feature selection, and the prediction results with different classification machines show significant improvements. Compared to other traditional feature selection methods, RAD provides higher Interpretability in the TCM field.