Reality mining in eHealth

Wlodarczak, Peter and Soar, Jeffrey and Ally, Mustafa (2015) Reality mining in eHealth. In: 4th International Health Information Science Conference (HIS 2015), 28-30 May 2015, Melbourne, Australia.

Abstract

There is increasing interest in Big Data analytics in health care. Behavioral health analytics is a care management technology that aims to improve the quality of care and reduce health care costs based capture and analysis of data on patient’s behavioral patterns. Big Data analytics of behavioral health data offers the potential of more precise and personalized treatment as well as monitor population-wide events such as epidemics.

Mobile phones are powerful social sensors that are usually physically close to users and leave digital traces of users’ behaviors and movement patterns. New Apps (application or piece of software) are emerging that passively collect and analyze mobile phone data of at-risk patients such as their location, calling and texting records and app usage, and can find deviations in a user’s daily patterns to detect that something is wrong before an event occurs. Data mining and machine learning techniques are adopted to analyze the “automated diaries” created by the smart phone and monitor the well-being of people. The App first learns a patients daily behavioral patterns using machine learning techniques. Once trained, the App detects deviations and alerts carers based on predictive models.

This paper describes the techniques used and algorithms for reality mining and predictive analysis used in eHealth Apps.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © Springer International Publishing Switzerland 2015.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 23 Jun 2016 23:37
Last Modified: 14 Feb 2018 01:57
Uncontrolled Keywords: reality mining; big data; machine learning; eHealth; predictive analytics; behavioral health analytics; mobile sensing
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
Socio-Economic Objective: C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: 10.1007/978-3-319-19156-0_1
URI: http://eprints.usq.edu.au/id/eprint/27426

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