A new prognostic scale for the early prediction of ischemic stroke recovery mainly based on traditional Chinese medicine symptoms and NIHSS score: a retrospective cohort study

TCM symptoms & signs with appearance rate no less than 5 %. In practical analysis we selected 57 TCM symptoms with the appearance rate ≥5 % from 157 TCM symptoms& signs except tongue and pulse. (CSV 1 kb

Combining Artificial Intelligence with Traditional Chinese Medicine for Intelligent Health Management

The growth of artificial intelligence (AI) is being referred to as the beginning of "the fourth industrial revolution". With the rapid development of hardware, algorithms, and applications, AI not only provides a new concept and relevant solutions to solve the problem of complexity science but also provides a new concept and method to promote the development of traditional Chinese medicine (TCM). In this study, based on the research and development of AI technology applications in biomedical and clinical diagnosis and treatment, we introduce AI technologies in current TCM research. This can have applications in intelligent clinical information acquisition, intelligent clinical decision, and efficacy evaluation of TCM; intelligent classification management, intelligent prescription, and drug research in Chinese herbal medicine; and health management. Furthermore, we propose a framework of "intelligent TCM" and outline its development prospects

高齢者の健康管理におけるウェアラブルデバイスと中国伝統医学の役割

早大学位記番号:新8258早稲田大

Misdiagnosis and undiagnosis due to pattern similarity in Chinese medicine: a stochastic simulation study using pattern differentiation algorithm

<p>Abstract</p> <p>Background</p> <p>Whether pattern similarity causes misdiagnosis and undiagnosis in Chinese medicine is unknown. This study aims to test the effect of pattern similarity and examination methods on diagnostic outcomes of pattern differentiation algorithm (PDA).</p> <p>Methods</p> <p>A dataset with 73 <it>Zangfu </it>single patterns was used with manifestations according to the Four Examinations, namely inspection (Ip), auscultation and olfaction (AO), inquiry (Iq) and palpation (P). PDA was applied to 100 true positive and 100 true negative manifestation profiles per pattern in simulation. Four runs of simulations were used according to the Four Examinations: Ip, Ip+AO, Ip+AO+Iq and Ip+AO+Iq+P. Three pattern differentiation outcomes were separated, namely correct diagnosis, misdiagnosis and undiagnosis. Outcomes frequencies, dual pattern similarity and pattern-dataset similarity were calculated.</p> <p>Results</p> <p>Dual pattern similarity was associated with Four Examinations (gamma = -0.646, <it>P </it>< 0.01). Combination of Four Examinations was associated (gamma = -0.618, <it>P </it>< 0.01) with decreasing frequencies of pattern differentiation errors, being less influenced by pattern-dataset similarity (Ip: gamma = 0.684; Ip+AO: gamma = 0.660; Ip+AO+Iq: gamma = 0.398; Ip+AO+Iq+P: gamma = 0.286, <it>P </it>< 0.01 for all combinations).</p> <p>Conclusion</p> <p>Applied in an incremental manner, Four Examinations progressively reduce the association between pattern similarity and pattern differentiation outcome and are recommended to avoid misdiagnosis and undiagnosis due to similarity.</p

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

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

Application of Metabolomics in Traditional Chinese Medicine Differentiation of Deficiency and Excess Syndromes in Patients with Diabetes Mellitus

Metabolic profiling is widely used as a probe in diagnosing diseases. In this study, the metabolic profiling of urinary carbohydrates was investigated using gas chromatography/mass spectrometry (GC/MS) and multivariate statistical analysis. The kernel-based orthogonal projections to latent structures (K-OPLS) model were established and validated to distinguish between subjects with and without diabetes mellitus (DM). The model was combined with subwindow permutation analysis (SPA) in order to extract novel biomarker information. Furthermore, the K-OPLS model visually represented the alterations in urinary carbohydrate profiles of excess and deficiency syndromes in patients with diabetes. The combination of GC/MS and K-OPLS/SPA analysis allowed the urinary carbohydrate metabolic characterization of DM patients with different traditional Chinese medicine (TCM) syndromes, including biomarkers different from non-DM patients. The method presented in this study might be a complement or an alternative to TCM syndrome research

Practice of Comparative Effectiveness Research to Identify Treatment Characteristics of Similar Chinese Patent Medicine for Angina Pectoris

Objective. Individualized application of TCM is not easy and may lead to undesirable results, such as poor effect or even adverse reactions. This trial aims to compare two common Chinese patent medicines with similar effects. Background of the Research. Four hospitals carried out the test at the same time in Tianjin city of China. Participants. 144 patients were involved in this study; all patients must meet the diagnostic criteria. Interventions. Qishen Yiqi pills, compound danshen pills, and their placebos; an efficacy analysis was conducted after the first medication and after crossover medication. Primary Outcome Measures. The primary index of end point includes Seattle Angina Questionnaire score-7 and score of 7-point Likert Scale; the curative effect was compared with minimal clinically important differences value. Result. Two drugs have their respective advantages in treating SAP. In practical application, the two drugs shall be discriminated in use based on patients’ specific symptoms. Trial Registration. Chinese clinical trials register is ChiCTR-TTRCC-14004406 (registered 23 March 2014)

Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning

Objective. To compare the signals of pulse diagnosis of fatty liver disease (FLD) patients and cirrhosis patients. Methods. After collecting the pulse waves of patients with fatty liver disease, cirrhosis patients, and healthy volunteers, we do pretreatment and parameters extracting based on harmonic fitting, modeling, and identification by unsupervised learning Principal Component Analysis (PCA) and supervised learning Least squares Regression (LS) and Least Absolute Shrinkage and Selection Operator (LASSO) with cross-validation step by step for analysis. Results. There is significant difference between the pulse diagnosis signals of healthy volunteers and patients with FLD and cirrhosis, and the result was confirmed by 3 analysis methods. The identification accuracy of the 1st principal component is about 75% without any classification formation by PCA, and supervised learning’s accuracy (LS and LASSO) was even more than 93% when 7 parameters were used and was 84% when only 2 parameters were used. Conclusion. The method we built in this study based on the combination of unsupervised learning PCA and supervised learning LS and LASSO might offer some confidence for the realization of computer-aided diagnosis by pulse diagnosis in TCM. In addition, this study might offer some important evidence for the science of pulse diagnosis in TCM clinical diagnosis