A contextual data mining approach toward assisting the treatment of anxiety disorders

  • Authors:

  • Theodor Chris Panagiotakopoulos;Dimitrios Panagiotis Lyras;Miltos Livaditis;Kyriakos N. Sgarbas;George C. Anastassopoulos;Dimitrios K. Lymberopoulos

  • Affiliations:

  • Department of Electrical and Computer Engineering, Wire Communications Laboratory, University of Patras, Patras, Greece;Department of Electrical and Computer Engineering, Artificial Intelligence Group, University of Patras, Patras, Greece;Department of Psychiatry, School of Medicine, Democritus University of Thrace, Alexandroupolis, Greece;Department of Electrical and Computer Engineering, Artificial Intelligence Group, University of Patras, Patras, Greece;Medical Informatics Laboratory, School of Medicine, Democritus University of Thrace, Alexandroupolis, Greece;Department of Electrical and Computer Engineering, Wire Communications Laboratory, University of Patras, Patras, Greece

  • Venue:
  • IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
  • Year:
  • 2010

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Abstract

Anxiety disorders are considered the most prevalent of mental disorders. Nevertheless, the exact reasons that provoke them to patients remain yet not clearly specified, while the literature concerning the environment for monitoring and treatment support is rather scarce warranting further investigation. Toward this direction, in this study a context-aware approach is proposed, aiming to provide medical supervisors with a series of applications and personalized services targeted to exploit the multiparameter contextual data collected through a long-term monitoring procedure. More specifically, an application that assists the archiving and retrieving of the patients' health records was developed, and four treatment supportive services were considered. The three of them focus on the discovery of possible associations between the patient's contextual data; the last service aims at predicting the stress level a patient might suffer from, in a given context. The proposed approach was experimentally evaluated quantitatively (in terms of computational efficiency and time requirements) and qualitatively by experts on the field of mental health domain. The feedback received was very encouraging and the proposed approach seems quite useful to the anxiety disorders' treatment.