A Contextual Data Mining Approach Toward Assisting the Treatment of Anxiety Disorders

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 investigat...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2010-05, Vol.14 (3), p.567-581
Main Authors: Panagiotakopoulos, Theodor Chris, Lyras, Dimitrios Panagiotis, Livaditis, Miltos, Sgarbas, Kyriakos N, Anastassopoulos, George C, Lymberopoulos, Dimitrios K
Format: Article
Language:English
Subjects:
Activities of Daily Living
Anxiety
Anxiety Disorders - therapy
Artificial Intelligence
Bayes Theorem
Biomedical monitoring
Computational efficiency
Context awareness
Context-aware services
Data mining
Data Mining - methods
Human factors
Humans
Laboratories
Life Style
machine learning
Medical treatment
Mental disorders
mental health
Models, Biological
Patient monitoring
Pattern Recognition, Automated
Precision Medicine
Psychology
ROC Curve
Stress, Psychological - therapy
user modeling
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Summary: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.
ISSN:1089-7771
2168-2194
1558-0032
2168-2208
DOI:10.1109/TITB.2009.2038905