Functional Behavioral Assessment Using the LERS Data Mining System—Strategies for Understanding Complex Physiological and Behavioral Patterns

Authors: Freeman R.L.¹; Grzymala-Busse J.W.²; Harvey M.³

Source: Journal of Intelligent Information Systems, Volume 21, Number 2, September 2003 , pp. 173-181(9)

Publisher: Springer

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Abstract:

Many individuals with mental retardation, autism, and other related disabilities lead lives that are significantly restricted because of problem behaviors such as self-injury and aggression. We processed two data sets, one describing heart rate patterns and the other describing the behavioral events of one subject diagnosed with severe mental retardation, visual impairments, and severe problem behavior. From these data sets the LERS data mining system induced certain and possible rule sets. In our research these rule sets were successfully used for interpretation, or, more specifically, to discover mechanisms of triggering specific physiological and behavioral patterns.

Keywords: self-injurious behavior; data mining; LERS; LEM2 algorithm

Language: English

Document Type: Research article

Affiliations: 1: Bureau of Child Research, Institute for Life Span Studies, University of Kansas, Lawrence, KS 66045, USA 2: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA. jerzy@lightning.eecs.ku.edu 3: John F. Kennedy Center, Vanderbilt University, Peabody College, Nashville, TN 3720, USA

Publication date: 2003-09-01