scholarly journals A Real-World Evidence Study for Distribution of Traditional Chinese Medicine Syndrome and Its Elements on Respiratory Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Fei Xu ◽  
Wengqiang Cui ◽  
Qing Kong ◽  
Zihui Tang ◽  
Jingcheng Dong
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Real World ◽  
Association Rule ◽  
Medical Information ◽  
Real World Data ◽  
Traditional Chinese Medicine Syndrome ◽  
The Core ◽  
Real World Evidence ◽  
Tcm Syndrome

Background. This study aimed to investigate the distribution and characteristics of traditional Chinese medicine (TCM) syndrome and its elements on respiratory diseases (RDs) based on real-world data (RWD). Methods. A real-world study was performed to explore the relationships among TCM syndrome and RDs based on electronic medical information. A total of 26,074 medical records with complete data were available for data analysis. Factor analyses were used to reduce dimensions of TCM syndrome elements and detect common factors. Additionally, cluster analyses were employed to assess combinations of TCM syndrome elements. Finally, association rule analyses were performed to investigate the structures of TCM syndrome elements to estimate the patterns of TCM syndrome. Results. A total of 27 TCM syndromes were extracted from RWD in this work. There were four TCM syndromes with >5.0% frequency based on the distribution frequency. The top five pathogenesis TCM syndrome elements were Tan, Huo, Feng, Qi_Xu, and Han. Factor analysis, cluster analysis, and association rule analysis demonstrated that Tan, Huo, Feng, Qi_Xu, Shen, and Fei were the core TCM syndrome elements. Conclusion. Four common Shi TCM syndromes on RDs were identified: Tan_Re_Yong_Fei, Tan_Zhuo_Zu_Fei, Feng_Re_Fan_Fei, and Feng_Han_Xi_Fei; two core common Xu TCM syndromes (Fei_Shen_Qi_Xu and Fei_Yin_Xu) and two core common Mix TCM syndromes (Fei_Pi_Qi_Xu-Tan_Shi_Yun_Fei and Fei_Shen_Qi_Xu-Tan_Yu_Zu_Fei) were also determined. The core TCM syndrome elements of Tan, Huo, Feng, Qi_Xu, Shen, and Fei were identified in this work.

A Real-World Data Analysis of The Distribution of Traditional Chinese Medicine Syndromes and Their Elements Among Bronchiectasis Patients

2020 ◽  
Author(s):  
Yijie Du ◽  
Qing Kong ◽  
Xiaoli Wu ◽  
Ye Jin ◽  
Zihui Tang ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Real World ◽  
Association Rule ◽  
Medical Information ◽  
Total Sample ◽  
Real World Data ◽  
World Data ◽  
The Core ◽  
Tcm Syndrome

Abstract Introduction: We sought to investigate the distribution of traditional Chinese medicine (TCM) syndromes and their elements among bronchiectasis patients using real-world data. Methods: A real-world study was performed to explore the relationship between TCM syndrome and bronchiectasis using electronic medical information from 1,113 patients in China. Factor analyses were used to reduce the dimensions of TCM syndrome elements and to detect common factors. Additionally, cluster analyses were employed to assess combinations of TCM syndrome elements. Finally, association rule analyses were performed to investigate the structures of TCM syndrome elements in order to estimate the patterns of TCM syndromes.Results: A total of 17 TCM syndrome elements were extracted using this method. There were four Shi TCM syndromes of Tan_Re_Yong_Fei (36.39%), Tan_Zhuo_Zu_Fei (12.94%), Gan_Huo_Fan_Fei (11.59%), and Feng_Re_Fan_Fei (11.32%) with >5.0% distribution frequency in total sample. The highest Xu TCM syndrome was Fei_Yin_Xu (18.24%). Factor analysis, cluster analysis, and association rule analysis found that Tan, Huo, Feng, Yin_Xu, Fei, and Gan were the core TCM syndrome elements.Conclusion: In this study, TCM Shi syndromes of Tan_Re_Yong_Fei, Tan_Zhuo_Zu_Fei, Gan_Huo_Fan_Fei, and Feng_Re_Fan_Fei were detected with a high frequency among bronchiectasis patients using real-world data, as was the TCM core Xu syndrome of Fei_Yin_Xu. The core elements of Huo, Tan, Feng, Yin_Xu, Fei and Gan were found across the entire sample.

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

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.

scholarly journals Model Organisms and Traditional Chinese Medicine Syndrome Models

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Shuang Ling ◽  
Jin-Wen Xu
Keyword(s):  
Animal Model ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Evaluation System ◽  
Pharmacological Action ◽  
Modern Medicine ◽  
Model Organisms ◽  
Traditional Chinese Medicine Syndrome ◽  
The Difference ◽  
Tcm Syndrome

Traditional Chinese medicine (TCM) is an ancient medical system with a unique cultural background. Nowadays, more and more Western countries due to its therapeutic efficacy are accepting it. However, safety and clear pharmacological action mechanisms of TCM are still uncertain. Due to the potential application of TCM in healthcare, it is necessary to construct a scientific evaluation system with TCM characteristics and benchmark the difference from the standard of Western medicine. Model organisms have played an important role in the understanding of basic biological processes. It is easier to be studied in certain research aspects and to obtain the information of other species. Despite the controversy over suitable syndrome animal model under TCM theoretical guide, it is unquestionable that many model organisms should be used in the studies of TCM modernization, which will bring modern scientific standards into mysterious ancient Chinese medicine. In this review, we aim to summarize the utilization of model organisms in the construction of TCM syndrome model and highlight the relevance of modern medicine with TCM syndrome animal model. It will serve as the foundation for further research of model organisms and for its application in TCM syndrome model.

scholarly journals The Quintessence of Traditional Chinese Medicine: Syndrome and Its Distribution among Advanced Cancer Patients with Constipation

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Chung-Wah Cheng ◽  
Annie O. L. Kwok ◽  
Zhao-Xiang Bian ◽  
Doris M. W. Tse
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Cancer Patients ◽  
Advanced Cancer ◽  
Cross Sectional Study ◽  
Advanced Cancer Patients ◽  
Cross Sectional ◽  
Traditional Chinese Medicine Syndrome ◽  
Blood Haemoglobin ◽  
Tcm Syndrome

Constipation is a common problem in advanced cancer patients; however, specific clinical guidelines on traditional Chinese medicine (TCM) syndrome (Zhang) are not yet available. In this cross-sectional study, the TCM syndromes distribution and their common symptoms and signs among 225 constipated advanced cancer patients were determined. Results showed that 127 patients (56.4%) and 7 patients (3.1%) were in deficient and excessive patterns, respectively, while 91 patients (40.4%) were in deficiency-excess complex. The distributions of the five syndromes were:Qideficiency (93.3%),Qistagnation (40.0%), blood (Yin) deficiency (28.9%), Yang deficiency (22.2%), and excess heat (5.8%). Furthermore, age, functional status, and level of blood haemoglobin were factors related to the type of TCM syndrome. A TCM prescription with the functions on replenishing the Deficiency, redirecting the flow ofQistagnation and moistening the dryness caused by the blood (Yin) deficiency can be made for the treatment of advance cancer patients with constipation. Robust trials are urgently needed for further justifying its efficacy and safety in evidence-based approaches.

The Chang Gung Research Database: Multi‐Institutional Real‐World Data Source for Traditional Chinese Medicine in Taiwan

2021 ◽  
Author(s):  
Shih‐Chieh Shao ◽  
Edward Chia‐Cheng Lai ◽  
Tse‐Hung Huang ◽  
Ming‐Jui Hung ◽  
Ming‐Shao Tsai ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Real World ◽  
Research Database ◽  
Real World Data ◽  
World Data ◽  
Data Source

scholarly journals Real-world evidence: perspectives on challenges, value, and alignment of regulatory and national health technology assessment data collection requirements

2021 ◽  
Vol 37 ◽  
Author(s):  
Hannah Sievers ◽  
Angelika Joos ◽  
Mickaël Hiligsmann
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
Real World ◽  
Health Technology ◽  
Practical Experience ◽  
Added Value ◽  
Real World Data ◽  
Conflicting Demands ◽  
Real World Evidence ◽  
Semistructured Interviews

Abstract Objective This study aims to assess stakeholder perceptions on the challenges and value of real-world evidence (RWE) post approval, the differences in regulatory and health technology assessment (HTA) real-world data (RWD) collection requirements under the German regulation for more safety in drug supply (GSAV), and future alignment opportunities to create a complementary framework for postapproval RWE requirements. Methods Eleven semistructured interviews were conducted purposively with pharmaceutical industry experts, regulatory authorities, health technology assessment bodies (HTAbs), and academia. The interview questions focused on the role of RWE post approval, the added value and challenges of RWE, the most important requirements for RWD collection, experience with registries as a source of RWD, perceptions on the GSAV law, RWE requirements in other countries, and the differences between regulatory and HTA requirements and alignment opportunities. The interviews were recorded, transcribed, and translated for coding in Nvivo to summarize the findings. Results All experts agree that RWE could close evidence gaps by showing the actual value of medicines in patients under real-world conditions. However, experts acknowledged certain challenges such as: (i) heterogeneous perspectives and differences in outcome measures for RWE generation and (ii) missing practical experience with RWD collected through mandatory registries within the German benefit assessment due to an unclear implementation of the GSAV. Conclusions This study revealed that all stakeholder groups recognize the added value of RWE but experience conflicting demands for RWD collection. Harmonizing requirements can be achieved through common postlicensing evidence generation (PLEG) plans and joint scientific advice to address uncertainties regarding evidence needs and to optimize drug development.

scholarly journals Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yiqing Zhao ◽  
Saravut J. Weroha ◽  
Ellen L. Goode ◽  
Hongfang Liu ◽  
Chen Wang
Keyword(s):  
Targeted Therapy ◽  
Data Quality ◽  
Real World ◽  
Genetic Information ◽  
Genetic Data ◽  
Real World Data ◽  
Rule Based ◽  
Clinical Notes ◽  
Real World Evidence ◽  
F Measure

Abstract Background Next-generation sequencing provides comprehensive information about individuals’ genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in the clinical decision-making process has not been examined extensively from a real-world, data-driven perspective. Through mining real-world data (RWD) from clinical notes, we could extract patients’ genetic information and further associate treatment decisions with genetic information. Methods We proposed a real-world evidence (RWE) study framework that incorporates context-based natural language processing (NLP) methods and data quality examination before final association analysis. The framework was demonstrated in a Foundation-tested women cancer cohort (N = 196). Upon retrieval of patients’ genetic information using NLP system, we assessed the completeness of genetic data captured in unstructured clinical notes according to a genetic data-model. We examined the distribution of different topics regarding BRCA1/2 throughout patients’ treatment process, and then analyzed the association between BRCA1/2 mutation status and the discussion/prescription of targeted therapy. Results We identified seven topics in the clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance. Our rule-based system achieved a precision of 0.87, recall of 0.93 and F-measure of 0.91. Our machine learning system achieved a precision of 0.901, recall of 0.899 and F-measure of 0.9 for four-topic classification and a precision of 0.833, recall of 0.823 and F-measure of 0.82 for seven-topic classification. We found in result-containing sentences, the capture of BRCA1/2 mutation information was 75%, but detailed variant information (e.g. variant types) is largely missing. Using cleaned RWD, significant associations were found between BRCA1/2 positive mutation and targeted therapies. Conclusions In conclusion, we demonstrated a framework to generate RWE using RWD from different clinical sources. Rule-based NLP system achieved the best performance for resolving contextual variability when extracting RWD from unstructured clinical notes. Data quality issues such as incompleteness and discrepancies exist thus manual data cleaning is needed before further analysis can be performed. Finally, we were able to use cleaned RWD to evaluate the real-world utility of genetic information to initiate a prescription of targeted therapy.

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