Healthcare analytics using network thinking

Document Type

Journal Article

Publication Date

2017

Keywords

Big data, Data visualization, Graph theory, Statistical thinking, Heart diseases

Abstract

Health data can be easily gathered via the Internet, and processed as well as managed efficiently by technology. The sheer volume and complexity of the data seem unconnected, meaningless or useless. But, in fact, the data are connected to a certain extent because it is believed that everything is connected under the philosophy of network thinking. The data will be valuable and meaningful if statistical thinking is applied to obtain insights from the data. Of course, the data would thus become useful for research, therapy, planning, policy-making, and so forth. Data visualization is a common tool to extract useful information from the data, but many novice data scientists have difficulty in gaining insights and seeing insidious relationships of data. This hinders their ability to derive intrinsic meanings of graphical data. In this Article, the author presents a critical review of current research in this area from three different perspectives: pedagogy, statistics and cognitive psychology. Arising from a synthesis of this research he proposes cognitive models of data visualization which helps to check whether data form patterns; search for data patterns; check whether data patterns have meaning; and interpret the meaning of data patterns within the context of healthcare analytics. Beyond the elementary analytics, it would better incorporate network thinking to build a healthcare surveillance system for smart living.

Source Publication

Smart Cities, Smart Systems and Smart Environment Awareness

First Page

141

Last Page

163

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