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:
DOWNLOAD EBOOK →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.