Real-world study: a potential new approach to effectiveness evaluation of traditional Chinese medicine interventions

Traditional Chinese Medicine (TCM) has a unique set of therapeutic methods for plagues. COVID-19 is a severe type of pneumonia caused by a new coronavirus, which manifests in fever, cough, headache, fatigue, difficulty breathing, and other symptoms. TCM has fully displayed its advantages of various approaches in this epidemic, including herbal decoction, patent herbs, aroma packets, acupuncture, massage, etc. These methods have played an essential role in the prevention, treatment, and nursing of COVID-19, not only in alleviating the early clinical symptoms of patients but reducing the progression from mild to severe symptoms. Thanks to the advantages of treating the pandemic, we should pay more attention to TCM modalities.

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Real-world evidence study for distribution of traditional Chinese medicine syndrome and its elements on chronic bronchitis in China

Traditional Medicine and Modern Medicine ◽

10.1142/s2575900019500150 ◽

2019 ◽

Vol 02 (04) ◽

pp. 155-163

Author(s):

Qing Kong ◽

Mihui Li ◽

Xuanfeng Qin ◽

Yubao Lv ◽

Zihui Tang

Keyword(s):

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Chronic Bronchitis ◽

Real World ◽

Association Rule ◽

Factor Analyses ◽

Real World Data ◽

Cluster Analyses ◽

The Core ◽

Tcm Syndrome

Objective: To investigate the distribution and characteristics of traditional Chinese medicine (TCM) syndromes and its elements on chronic bronchitis (CB) based on real-world data (RWD) so as to optimize the treatment strategies. Methods: A real-world study based on 2207 medical records collected from five hospitals in China, to explore the relationship between TCM syndrome and CB using the big data methods. Factor analyses were used to reduce the dimensions of TCM syndrome elements and found common factors. Additionally, cluster analyses were performed to value combinations of TCM syndrome element. Finally, association rule analyses were employed to assess the structures of TCM syndromes elements and estimate the patterns of TCM syndrome. Results: A total of 21 TCM syndromes were extracted from RWD in this work. There were four TCM syndromes consisting of Tan_Zhuo_Zu_Fei, Tan_Re_Yong_Fei, Feng_Han_Xi_Fei, and Feng_Re_Fan_Fei with [Formula: see text]% frequency based on the distribution frequency. The two top Xu TCM syndromes of Fei_Yin_Xu and Fei_Shen_Qi_Xu were identified. The top six pathogenesis TCM syndrome elements were Tan, Huo, Feng, Han, Qi_Xu, and Yin_Xu. Factor analyses, cluster analyses, and association rule analyses demonstrated that Tan, Huo, Feng, Han, Qi-Xu, Yin-Xu, Fei, and Shen were the core TCM syndrome elements. Conclusion: The four common Shi TCM syndromes of Tan_Zhuo_Zu_Fei, Tan_Re_Yong_Fei, Feng_Han_Xi_Fei, and Feng_Re_Fan_Fei for CB were detected in the real world study, and the two Xu TCM syndromes of Fei_Yin_Xu and Fei_Shen_Qi_Xu were identified. The Mix TCM syndrome of Fei_Pi_Qi_Xu_Tan_Shi_Yun_Fei was the main syndrome. The core TCM syndrome elements of Tan, Huo, Feng, Han, Qi_Xu, and Yin_Xu, Fei, and Shen were determined in the entire sample.

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Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

BioMed Research International ◽

10.1155/2021/5556057 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Yang Xiang ◽

Lai Shujin ◽

Chang Hongfang ◽

Wen Yinping ◽

Yu Dawei ◽

...

Keyword(s):

Diabetes Mellitus ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Fundus Photography ◽

Diabetes Diagnosis ◽

Medical Resources ◽

New Approach

In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to transport. Diabetes mellitus is a chronic disease that affects public health globally. Although diabetes mellitus can be accurately diagnosed using conventional methods, these methods require the collection of data in a clinical setting and are unlikely to be feasible in areas with few medical resources. This technique combines an analysis of fundus photography of the physical and physiological features of the patient, namely, the tongue and the pulse, which are used in Traditional Chinese Medicine. A random forest algorithm was used to analyze the data, and the accuracy, precision, recall, and F1 scores for the correct classification of diabetes were 0.85, 0.89, 0.67, and 0.76, respectively. The proposed technique for diabetes diagnosis offers a new approach to the diagnosis of diabetes, in that it may be convenient in regions that lack medical resources, where the early detection of diabetes is difficult to achieve.

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