Reality mining in eHealth
Reality mining in eHealth
|Authors||Wlodarczak, Peter (Author), Soar, Jeffrey (Author) and Ally, Mustafa (Author)|
|Editors||Yin, Xiaoxia, Ho, Kendall, Zeng, Daniel, Aickelin, Uwe, Zhou, Rui and Wang, Hua|
|Journal or Proceedings Title||Proceedings of the 4th International Health Information Science Conference (HIS 2015)|
|Journal Citation||9085, pp. 1-6|
|Number of Pages||6|
|Place of Publication||Switzerland|
|Digital Object Identifier (DOI)||https://doi.org/10.1007/978-3-319-19156-0_1|
|Web Address (URL) of Paper||http://www.springer.com/gp/book/9783319191553|
|Conference/Event||4th International Health Information Science Conference (HIS 2015)|
4th International Health Information Science Conference (HIS 2015)
28 to end of 30 May 2015
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.
|Keywords||reality mining; big data; machine learning; eHealth; predictive analytics; behavioral health analytics; mobile sensing|
|ANZSRC Field of Research 2020||460999. Information systems not elsewhere classified|
© Springer International Publishing Switzerland 2015.
|Byline Affiliations||School of Management and Enterprise|
|Series||» Information Systems and Applications, incl. Internet/Web, and HCI|
|Institution of Origin||University of Southern Queensland|
|Book Title||Health Information Science|
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