Data Analytics For Traditional Chinese Medicine Research

This contributed volume explores how data mining, machine learning, and similar statistical techniques can analyze the types of problems arising from Traditional Chinese Medicine (TCM) research. The book focuses on the study of clinical data and the analysis of herbal data. Challenges addressed include diagnosis, prescription analysis, ingredient discoveries, network based mechanism deciphering, pattern-activity relationships, and medical informatics. Each author demonstrates how they made use of machine learning, data mining, statistics and other analytic techniques to resolve their research challenges, how successful if these techniques were applied, any insight noted and how these insights define the most appropriate future work to be carried out. Readers are given an opportunity to understand the complexity of diagnosis and treatment decision, the difficulty of modeling of efficacy in terms of herbs, the identification of constituent compounds in an herb, the relationship between these compounds and biological outcome so that evidence-based predictions can be made. Drawing on a wide range of experienced contributors, Data Analytics for Traditional Chinese Medicine Research is a valuable reference for professionals and researchers working in health informatics and data mining. The techniques are also useful for biostatisticians and health practitioners interested in traditional medicine and data analytics.

Recognized as an essential component of Chinese culture, Traditional Chinese Medicine (TCM) is both an ancient medical system and one still used widely in China today. TCM’s independently evolved knowledge system is expressed mainly in the Chinese language and the information is frequently only available through ancient classics and confidential family records, making it difficult to utilize. The major concern in TCM is how to consolidate and integrate the data, enabling efficient retrieval and discovery of novel knowledge from the dispersed data. Computational approaches such as data mining, semantic reasoning and computational intelligence have emerged as innovative approaches for the reservation and utilization of this knowledge system. Typically, this requires an inter-disciplinary approach involving Chinese culture, computer science, modern healthcare and life sciences. This book examines the computerization of TCM information and knowledge to provide intelligent resources and supporting evidences for clinical decision-making, drug discovery, and education. Recent research results from the Traditional Chinese Medicine Informatics Group of Zhejiang University are presented, gathering in one resource systematic approaches for massive data processing in TCM. These include the utilization of modern Semantic Web and data mining methods for more advanced data integration, data analysis and integrative knowledge discovery. This book will appeal to medical professionals, life sciences students, computer scientists, and those interested in integrative, complementary, and alternative medicine. Interdisciplinary book bringing together Traditional Chinese Medicine and computer scientists Introduces novel network technologies to Traditional Chinese Medicine informatics Provides theory and practical examples and case studies of new techniques

The practice of Traditional Chinse Medicine (TCM) has been gaining a wider acceptance worldwide in recent decades. The global TCM market was estimated to be worth nearly US$60 billion in 2012 with the China market alone projected by Helmut Kaiser Consultancy to exceed US$121 billion in 2025. HerbMiners aims to make TCM healthcare smarter by unlocking the value of clinical data. Its research process includes the application of data mining to reveal relationships between symptoms, illnesses, herbs and prescriptions; and using artificial intelligence to learn about TCM diagnosis differentiation and prescriptions from TCM practitioners. It also provides TCM Advisor (TCMA), an integrated software solution that assists hospitals and clinics with TCM practice modernization and patient record digitalization. TCMA is currently used by a large number of private TCM clinics and more than 80% of non-governmental organizations in Hong Kong that provide TCM service, as well as sites in the United States, Canada, Australia, Singapore, Philippines and Macau. While the first generation TCMA system – developed in-house on the Microsoft Windows .Net framework with a data capture module running on the Windows Azure cloud platform – enabled HerbMiners to tap into clinical data streams, the hybrid application architecture was laborious to support on-site, limiting the company’s ability to take on more TCM clinics and diverting staff resources from its core research activities. HerbMiners Big data analytics is the use of advanced analytic techniques against very large, diverse Integrative medicine data sets that include different types such as structured/unstructured and streaming/batch/images/data mining, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale. Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.

A Western-Based Approach to Analyzing TCMs In recent years, many pharmaceutical companies and clinical research organizations have been focusing on the development of traditional Chinese (herbal) medicines (TCMs) as alternatives to treating critical or life-threatening diseases and as pathways to personalized medicine. Quantitative Methods for Traditional Chinese Medicine Development is the first book entirely devoted to the design and analysis of TCM development from a Western perspective, i.e., evidence-based clinical research and development. The book provides not only a comprehensive summary of innovative quantitative methods for developing TCMs but also a useful desk reference for principal investigators involved in personalized medicine. Written by one of the world’s most prominent biostatistics researchers, the book connects the pharmaceutical industry, regulatory agencies, and academia. It presents a state-of-the-art examination of the subject for: Scientists and researchers who are engaged in pharmaceutical/clinical research and development of TCMs Those in regulatory agencies who make decisions in the review and approval process of TCM regulatory submissions Biostatisticians who provide statistical support to assess clinical safety and effectiveness of TCMs and related issues regarding quality control and assurance as well as to test for consistency in the manufacturing processes for TCMs This book covers all of the statistical issues encountered at various stages of pharmaceutical/clinical development of a TCM. It explains regulatory requirements; product specifications and standards; and various statistical techniques for evaluation of TCMs, validation of diagnostic procedures, and testing consistency. It also contains an entire chapter of case studies and addresses critical issues in TCM development and FAQs from a regulatory perspective.

This book is a practical guide to the evidence-based medicine (EBM) research methods that are applicable to Chinese medicine (CM). It includes 3 parts: clinical research, healthcare data research and laboratory research. In each chapter, it explains how to acquire, analyze and evaluate a specific kind of CM research result. Authors exemplify the most important methods with successful cases in transforming reliable data into new knowledge of CM. This book emphasizes the requirements for CM researchers to design and conduct CM studies by following the best available principles, standards and guidelines.

This book focuses on the multi-omics big-data integration, the data-mining techniques and the cutting-edge omics researches in principles and applications for a deep understanding of Traditional Chinese Medicine (TCM) and diseases from the following aspects: (1) Basics about multi-omics data and analytical methods for TCM and diseases. (2) The needs of omics studies in TCM researches, and the basic background of omics research in TCM and disease. (3) Better understanding of the multi-omics big-data integration techniques. (4) Better understanding of the multi-omics big-data mining techniques, as well as with different applications, for most insights from these omics data for TCM and disease researches. (5) TCM preparation quality control for checking both prescribed and unexpected ingredients including biological and chemical ingredients. (6) TCM preparation source tracking. (7) TCM preparation network pharmacology analysis. (8) TCM analysis data resources, web services, and visualizations. (9) TCM geoherbalism examination and authentic TCM identification. Traditional Chinese Medicine has been in existence for several thousands of years, and only in recent tens of years have we realized that the researches on TCM could be profoundly boosted by the omics technologies. Devised as a book on TCM and disease researches in the omics age, this book has put the focus on data integration and data mining methods for multi-omics researches, which will be explained in detail and with supportive examples the “What”, “Why” and “How” of omics on TCM related researches. It is an attempt to bridge the gap between TCM related multi-omics big data, and the data-mining techniques, for best practice of contemporary bioinformatics and in-depth insights on the TCM related questions.

This volume presents texts written by Austrian and Chinese experts in the field of Traditional Chinese Medicine. The issue the authors worked on is the basic problem how to make a different system of medical thinking plausible for the Western world, especially for Western medicine. This issue is considered from different viewpoints - from the viewpoint of Western medicine that is familiar with Chinese medicine and contrariwise from the viewpoint of Chinese Medicine that is familiar with its Western counterpart and from a philosophical viewpoint. In this way both differences in the theoretical systems of Western and Chinese medicine and problems of adequate translation are profundly discussed.

This book gathers a selection of peer-reviewed papers presented at the first Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2019) conference, held in Shengyang, China, on 28–29 December 2019. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.