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.

This study examines a variety of artificial neural network (ANN) models in terms of their classification efficiency in an orthopedic disease, namely osteoporosis. Osteoporosis risk prediction may be viewed as a pattern classification problem, based on a set of clinical parameters. Multilayer perceptrons (MLPs) and probabilistic neural networks (PNNs) were used in order to face the osteoporosis risk factor prediction. This approach is the first computational intelligence technique based on ANNs for osteoporosis risk study on Greek population. MLPs and PNNs are both feed-forward networks; however, their modus operandi is different. Various MPL architectures were examined after modifying the number of nodes in the hidden layer, the transfer functions and the learning algorithms. Moreover, PNNs were implemented with spread values ranging from 0.1 to 50, and 4 or 2 neurons in output layer, according to coding of osteoporosis desired outcome. The obtained results lead to the conclusion that the PNNs outperform to MLPs, thus they are proved as appropriate computation intelligence technique for osteoporosis risk factor prediction. Furthermore, the overfitting problem was more frequent to MLPs, contrary to PNNs as their spread value increased. The aim of proposed PNN is to assist specialists in osteoporosis prediction, avoiding unnecessary further testing with bone densitometry.