Development and evaluation of an inquiry scale for diagnosis of heart system syndromes in traditional Chinese medicine

Gp, Liu; Yq, Wang; Y, Dong; Zhao, Nancy Q.; Zx, Xu; Ff, Li; Hx, Yan; Qian, Peter Z. G.; Guo, Rong; Zhang, Xiao Dong; D, Di

doi:10.3736/jcim20090103

Cited by 12 publications

(2 citation statements)

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“…In this paper, a heart system inquiry diagnosis scale [14] is designed, in which the symptoms are defined clearly, and the detailed collecting methods are listed. The scale is shown in [Additional file 1].…”

Section: Methodsmentioning

confidence: 99%

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

Wang

et al. 2010

BMC Complement Altern Med

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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.

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Section: Methodsmentioning

confidence: 99%

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

Wang

et al. 2010

BMC Complement Altern Med

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“…In this study, a structured questionnaire [5] was used to collect demographic information from participants. The questionnaire encompassed essential details such as gender, age, height, weight and additional pertinent data.…”

Section: 2data Collectionmentioning

confidence: 99%

Fusing Pulse and ECG Data for Coronary Heart Disease and Complications Identification

hong,

GUO,

et al. 2024

Preprint

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Objectives: This study aimed to explore the potential of combining wrist pulse with limb lead electrocardiogram (ECG) data to develop an identification model for coronary heart disease (CHD) and its associated comorbidities. Methods: We utilized a pulse-detecting device equipped with a pressure sensor and an ECG sensor to simultaneously collect wrist pulse and limb lead ECG signals from patients with coronary heart disease (CHD) and various comorbidities, including hypertension and diabetes. Time-domain analysis was applied to extract features such as time-domain parameters and pulse rate variability from the wrist pulse signals, as well as time-domain parameters and heart rate variability from the limb lead ECG signals. We implemented the random forest (RF) machine learning algorithm, to establish disease identification models based on these features, and evaluated their performance. Results: The results indicated that the disease identification model which incorporated features from both pulse and ECG signals, exhibited improvements of 1.99%, 3.13%, 3.78% and 3.32% in terms of accuracy, average precision, average recall and F1 value, respectively, when compared to the model based solely on pulse features. Furthermore, when compared to the ECG-based model, the results were improved by 3.99%, 3.13%, 3.78% and 3.32% respectively. Conclusions: The fusion of information from multiple sources enhances the reliability of decision-making of the system. This approach presents a novel method for managing cardiovascular diseases and offers insights into the application and promotion of wearable pulse-detecting products.

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CIGB-300: A Promising Anti-Casein Kinase 2 (CK2) Peptide for Cancer Targeted Therapy

Perea

Perera

Baladrón

et al. 2015

Protein Kinase CK2 Cellular Function in Normal and Disease States

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Development and evaluation of an inquiry scale for diagnosis of heart system syndromes in traditional Chinese medicine

Abstract: The research on the scale for inquiry in TCM indicates that it is feasible for the standardization of inquiry scale for diagnosis of heart system syndromes in TCM, offering a reference for research on the inquiry scales for other systems.

Cited by 12 publications

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

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

Fusing Pulse and ECG Data for Coronary Heart Disease and Complications Identification

CIGB-300: A Promising Anti-Casein Kinase 2 (CK2) Peptide for Cancer Targeted Therapy

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