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.
Next generation sequencing (NGS) platforms are replacing traditional molecular biology protocols like cloning and Sanger sequencing. However, accuracy of NGS platforms has rarely been measured when quantifying relative frequencies of genotypes or taxa within populations. Here we developed a new bioinformatic pipeline (QRS) that pools similar sequence variants and estimates their frequencies in NGS data sets from populations or communities. We tested whether the estimated frequency of representative sequences, generated by 454 amplicon sequencing, differs significantly from that obtained by Sanger sequencing of cloned PCR products. This was performed by analysing sequence variation of the highly variable first internal transcribed spacer (ITS1) of the ichthyosporean Caullerya mesnili, a microparasite of cladocerans of the genus Daphnia. This analysis also serves as a case example of the usage of this pipeline to study within-population variation. Additionally, a public Illumina data set was used to validate the pipeline on community-level data. Overall, there was a good correspondence in absolute frequencies of C. mesnili ITS1 sequences obtained from Sanger and 454 platforms. Furthermore, analyses of molecular variance (amova) revealed that population structure of C. mesnili differs across lakes and years independently of the sequencing platform. Our results support not only the usefulness of amplicon sequencing data for studies of within-population structure but also the successful application of the QRS pipeline on Illumina-generated data. The QRS pipeline is freely available together with its documentation under GNU Public Licence version 3 at http://code.google.com/p/quantification-representative-sequences.
While anxiety disorders exhibit an impressive spread especially in western societies, context-awareness seems a promising technology to provide assistance to physicians in psychotherapy sessions. In the present paper an approach addressing the assistance of the anxiety disorders' treatment is proposed. The suggested method employs the a priori association rule mining algorithm in order to achieve dynamic update of patient profiles according to generated rules describing the underlying relations between patients' main context conditions and their stress level. This method was evaluated by therapists specializing in the mental health domain and the feedback received was very encouraging with respect to the assistance dynamic patient profiles offer, during CBT sessions.
This paper addresses the problem of automatic induction of the normalized form (lemma) of regular and mildly irregular words with no direct supervision using language-independent algorithms. More specifically, two string distance metric models (i.e. the Levenshtein Edit Distance algorithm and the Dice Coefficient similarity measure) were employed in order to deal with the automatic word lemmatization task by combining two alignment models based on the string similarity and the most frequent inflectional suffixes. The performance of the proposed model has been evaluated quantitatively and qualitatively. Experiments were performed for the Modern Greek and English languages and the results, which are set within the state-of-the-art, have showed that the proposed model is robust (for a variety of languages) and computationally efficient. The proposed model may be useful as a pre-processing tool to various language engineering and text mining applications such as spell-checkers, electronic dictionaries, morphological analyzers etc.
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