Discovery and diagnosis of behavioral transitions in patient event streams

  • Authors:

  • William N. Robinson;Arash Akhlaghi;Tianjie Deng;Ali Raza Syed

  • Affiliations:

  • Georgia State University, Atlanta, GA;Georgia State University, Atlanta, GA;Georgia State University, Atlanta, GA;Georgia State University, Atlanta, GA

  • Venue:
  • ACM Transactions on Management Information Systems (TMIS)
  • Year:
  • 2012

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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.