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Discovery and diagnosis of behavioral transitions in patient event streams

Discovery and diagnosis of behavioral transitions in patient event streams Discovery and Diagnosis of Behavioral Transitions in Patient Event Streams WILLIAM N. ROBINSON, ARASH AKHLAGHI, TIANJIE DENG, and ALI RAZA SYED, Georgia State University 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, realtime 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 signi cantly from nearby models ”as de ned by quality metrics ”the user ™s behavior is then ‚agged as a signi cant behavioral change. The speci c 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. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications ”Data mining; http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

Discovery and diagnosis of behavioral transitions in patient event streams

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
2158-656X
DOI
10.1145/2151163.2151167
Publisher site
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Abstract

Discovery and Diagnosis of Behavioral Transitions in Patient Event Streams WILLIAM N. ROBINSON, ARASH AKHLAGHI, TIANJIE DENG, and ALI RAZA SYED, Georgia State University 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, realtime 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 signi cantly from nearby models ”as de ned by quality metrics ”the user ™s behavior is then ‚agged as a signi cant behavioral change. The speci c 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. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications ”Data mining;

Journal

ACM Transactions on Management Information Systems (TMIS) – Association for Computing Machinery

Published: Apr 1, 2012

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