Big Data Analytics and Data Mining of Prescribing Patterns of Integrative Medicine Volume 1

Big Data Analytics and Data Mining of Prescribing Patterns of Integrative Medicine Volume 1

Author: Dr. Wilfred W.K. Lin

Publisher: Dr. Wilfred W.K. Lin

Published: 2020-09-30

Total Pages: 268

ISBN-13:

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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.


Book Synopsis Big Data Analytics and Data Mining of Prescribing Patterns of Integrative Medicine Volume 1 by : Dr. Wilfred W.K. Lin

Download or read book Big Data Analytics and Data Mining of Prescribing Patterns of Integrative Medicine Volume 1 written by Dr. Wilfred W.K. Lin and published by Dr. Wilfred W.K. Lin. This book was released on 2020-09-30 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Behavioral Science & Policy, Volume 4

Behavioral Science & Policy, Volume 4

Author: Steven Patierno

Publisher: Brookings Institution Press

Published: 2019-01-29

Total Pages: 69

ISBN-13: 0815737068

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The success of nearly all public- and private-sector policies hinges on the behavior of individuals, groups, and organizations. Today, such behaviors are better understood than ever, thanks to a growing body of practical behavioral science research. However, policymakers often are unaware of behavioral science findings that may help them craft and execute more effective and efficient policies. The pages of this journal will become a meeting ground: a place where scientists and non-scientists can encounter clearly described behavioral research that can be put into action. By design, the scope of Behavioral Science & Policy is broad, with topics spanning health care, financial decisionmaking, energy and the environment, education and culture, justice and ethics, and work place practices. Contributions will be made by researchers with expertise in psychology, sociology, law, behavioral economics, organization science, decision science, and marketing. The journal is a key offering of the Behavioral Science & Policy Association in partnership with the Brookings Institution. The mission of BSPA is to foster dialog between social scientists, policymakers, and other practitioners in order to promote the application of rigorous empirical behavioral science in ways that serve the public interest. BSPA does not advance a particular agenda or political perspective.


Book Synopsis Behavioral Science & Policy, Volume 4 by : Steven Patierno

Download or read book Behavioral Science & Policy, Volume 4 written by Steven Patierno and published by Brookings Institution Press. This book was released on 2019-01-29 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: The success of nearly all public- and private-sector policies hinges on the behavior of individuals, groups, and organizations. Today, such behaviors are better understood than ever, thanks to a growing body of practical behavioral science research. However, policymakers often are unaware of behavioral science findings that may help them craft and execute more effective and efficient policies. The pages of this journal will become a meeting ground: a place where scientists and non-scientists can encounter clearly described behavioral research that can be put into action. By design, the scope of Behavioral Science & Policy is broad, with topics spanning health care, financial decisionmaking, energy and the environment, education and culture, justice and ethics, and work place practices. Contributions will be made by researchers with expertise in psychology, sociology, law, behavioral economics, organization science, decision science, and marketing. The journal is a key offering of the Behavioral Science & Policy Association in partnership with the Brookings Institution. The mission of BSPA is to foster dialog between social scientists, policymakers, and other practitioners in order to promote the application of rigorous empirical behavioral science in ways that serve the public interest. BSPA does not advance a particular agenda or political perspective.

Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications

Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications

Author: Management Association, Information Resources

Publisher: IGI Global

Published: 2019-12-06

Total Pages: 2071

ISBN-13: 1799812057

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Advancements in data science have created opportunities to sort, manage, and analyze large amounts of data more effectively and efficiently. Applying these new technologies to the healthcare industry, which has vast quantities of patient and medical data and is increasingly becoming more data-reliant, is crucial for refining medical practices and patient care. Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications is a vital reference source that examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations. Highlighting a range of topics such as data security and privacy, health informatics, and predictive analytics, this multi-volume book is ideally designed for doctors, hospital administrators, nurses, medical professionals, IT specialists, computer engineers, information technologists, biomedical engineers, data-processing specialists, healthcare practitioners, academicians, and researchers interested in current research on the connections between data analytics in the field of medicine.


Book Synopsis Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources

Download or read book Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2019-12-06 with total page 2071 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in data science have created opportunities to sort, manage, and analyze large amounts of data more effectively and efficiently. Applying these new technologies to the healthcare industry, which has vast quantities of patient and medical data and is increasingly becoming more data-reliant, is crucial for refining medical practices and patient care. Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications is a vital reference source that examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations. Highlighting a range of topics such as data security and privacy, health informatics, and predictive analytics, this multi-volume book is ideally designed for doctors, hospital administrators, nurses, medical professionals, IT specialists, computer engineers, information technologists, biomedical engineers, data-processing specialists, healthcare practitioners, academicians, and researchers interested in current research on the connections between data analytics in the field of medicine.

Business Engineering and Service Design, Second Edition, Volume I

Business Engineering and Service Design, Second Edition, Volume I

Author: Oscar Barros

Publisher: Business Expert Press

Published: 2016-10-14

Total Pages: 203

ISBN-13: 1631575694

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This book provides the foundations of BE, reviews the disciplines integrated within its methodology, and presents plentiful evidence of its power by giving detailed application cases, including impressive results in private and public situations.


Book Synopsis Business Engineering and Service Design, Second Edition, Volume I by : Oscar Barros

Download or read book Business Engineering and Service Design, Second Edition, Volume I written by Oscar Barros and published by Business Expert Press. This book was released on 2016-10-14 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the foundations of BE, reviews the disciplines integrated within its methodology, and presents plentiful evidence of its power by giving detailed application cases, including impressive results in private and public situations.

Big Data Analytics in Bioinformatics and Healthcare

Big Data Analytics in Bioinformatics and Healthcare

Author: Wang, Baoying

Publisher: IGI Global

Published: 2014-10-31

Total Pages: 552

ISBN-13: 1466666129

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As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.


Book Synopsis Big Data Analytics in Bioinformatics and Healthcare by : Wang, Baoying

Download or read book Big Data Analytics in Bioinformatics and Healthcare written by Wang, Baoying and published by IGI Global. This book was released on 2014-10-31 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.

Data Analytics for Traditional Chinese Medicine Research

Data Analytics for Traditional Chinese Medicine Research

Author: Josiah Poon

Publisher: Springer

Published: 2016-09-03

Total Pages: 0

ISBN-13: 9783319346298

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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.


Book Synopsis Data Analytics for Traditional Chinese Medicine Research by : Josiah Poon

Download or read book Data Analytics for Traditional Chinese Medicine Research written by Josiah Poon and published by Springer. This book was released on 2016-09-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Big Data Analytics

Big Data Analytics

Author: Arun K. Somani

Publisher: CRC Press

Published: 2017-10-30

Total Pages: 399

ISBN-13: 1351180320

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The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.


Book Synopsis Big Data Analytics by : Arun K. Somani

Download or read book Big Data Analytics written by Arun K. Somani and published by CRC Press. This book was released on 2017-10-30 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.

Big Data Analytics in Healthcare

Big Data Analytics in Healthcare

Author: Anand J. Kulkarni

Publisher: Springer Nature

Published: 2019-10-01

Total Pages: 187

ISBN-13: 3030316726

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This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms.


Book Synopsis Big Data Analytics in Healthcare by : Anand J. Kulkarni

Download or read book Big Data Analytics in Healthcare written by Anand J. Kulkarni and published by Springer Nature. This book was released on 2019-10-01 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations. The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms.

Data Science, AI, and Machine Learning in Drug Development

Data Science, AI, and Machine Learning in Drug Development

Author: Harry Yang

Publisher: CRC Press

Published: 2022-10-04

Total Pages: 335

ISBN-13: 100065267X

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The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise


Book Synopsis Data Science, AI, and Machine Learning in Drug Development by : Harry Yang

Download or read book Data Science, AI, and Machine Learning in Drug Development written by Harry Yang and published by CRC Press. This book was released on 2022-10-04 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise

AI and healthcare financial management (HFM) towards sustainable development

AI and healthcare financial management (HFM) towards sustainable development

Author: Ananth Rao

Publisher: Frontiers Media SA

Published: 2023-05-25

Total Pages: 163

ISBN-13: 2832510752

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Book Synopsis AI and healthcare financial management (HFM) towards sustainable development by : Ananth Rao

Download or read book AI and healthcare financial management (HFM) towards sustainable development written by Ananth Rao and published by Frontiers Media SA. This book was released on 2023-05-25 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: