research-article

Discovery and diagnosis of behavioral transitions in patient event streams

Authors:

William N. Robinson,

Arash Akhlaghi,

Ali Raza SyedAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 3, Issue 1

Article No.: 4, Pages 1 - 28

https://doi.org/10.1145/2151163.2151167

Published: 10 April 2012 Publication History

Abstract

Users with cognitive impairments use assistive technology (AT) as part of a clinical treatment plan. As the AT interface is manipulated, data stream mining techniques are used to monitor user goals. In this context, real-time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment, as the user learns his or her personalized emailing system. When the quality of some data-mined models varies significantly from nearby models—as defined by quality metrics—the user's behavior is then flagged as a significant behavioral change. The specific changes in user behavior are then characterized by differencing the data-mined decision tree models. This article describes how model quality monitoring and decision tree differencing can aid in recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The technique may be more widely applicable to other real-time data-intensive analysis problems.

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Discovery and diagnosis of behavioral transitions in patient event streams

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 3, Issue 1

April 2012

119 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/2151163

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Publication History

Published: 10 April 2012

Accepted: 01 January 2012

Revised: 01 December 2011

Received: 01 June 2011

Published in TMIS Volume 3, Issue 1

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https://doi.org/10.4018/978-1-7998-2310-0.ch011
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