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

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.

Measurement of Symptom Change Following Web-Based Psychotherapy: Statistical Characteristics and Analytical Methods for Measuring and Interpreting Change (Preprint)

“…Second, the current findings support a well-established statistical idea posing that the selection of a statistical analysis must match the characteristics of the dataset in order to arrive at valid and accurate statistical measurement, interpretation and conclusions (Flay et al, 2005;Field & Wilcox, 2017 (Castellani et al, 2016;Panagiotakopoulos et al, 2010).…”

“…For example, under circumstances where change is proportional in nature, the selection of a proportional statistical analysis can greatly increase the accuracy and validity of estimating longitudinal clinical change (Fitzmaurice & Laird, 2012;Liang and Zenger, 1986), the detection of moderators of symptom change (Castellani, Rajaram, Gunn, & Griffiths, 2016), the classification of subgroups, such as remitters or non-responders (Panagiotakopoulos, Lyras, Livaditis, Sgarbas, Anastassopoulos & Lymberopoulos, 2010), as well as the ability to research other objectives (Pocock, Clayton & Stone, 2015). For this reason, the function of symptom change must be researched, and more clearly understood.…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…Alzheimer's Disease COMPASS [200], SVM [198,200], DT [200], Genetic Algorithm [199], NN [214] Imaging [198,200], Biological [199], Smart Meter [214] Anxiety BN [226], ARM [226], DT [227,228], Regression [228], RF [228], kmeans clustering [229], NB [230], SVM [231] Electronic Health Records [226], Survey [227,230] Parkinson's Disease SVM [209] Imaging [209], Clinical Assessment [209] Post-traumatic Stress Disorder k-means clustering [229], kNN [250], NN [250], NLP [251], RF [201], Regression [201], SVM [201,250] Audio [250], Biological [201], Clinical Notes [251], Clinical Assessment [201], Social Media [229] Psychosis Gaussian Processes [206], SVM [207,208] Biological [206], Clinical Assessment [206], Survey [207,…”

Data mining approach for diagnose of anxiety disorder

“…al. [1] has Proposed to use Decision trees instead of association rule which can be used in order to better distinguish between each patient's stress provoking Contexts and environmental settings that result into serenity. Caregiving social network (CSN) is an effective means of assistance to the treatment of anxiety disorder.…”