The final publication is available at IOS Press through http://dx.doi.org/10.3233/THC-161289BACKGROUND AND OBJECTIVE: Major depressive disorder causes more human suffering than any other disease affecting humankind. It has a high prevalence and it is predicted that it will be among the three leading causes of disease burden by 2030. The prevalence of depression, all of its social and personal costs, and its recurrent characteristics, put heavy constraints on the ability of the public healthcare system to provide sufficient support for patients with depression. In this research, a model for continuous monitoring and tracking of depression in both short-term and long-term periods is presented. This model is based on a new qualitative reasoning approach. METHOD: This paper describes the patient assessment unit of a major depression monitoring system that has three modules: a patient progress module, based on a qualitative reasoning model; an analysis module, based on expert knowledge and a rules-based system; and the communication module. These modules base their reasoning mainly on data of the patient's mood and life events that are obtained from the patient's responses to specific questionnaires (PHQ-9, M.I.N.I. and Brugha). The patient assessment unit provides synthetic and useful information for both patients and physicians, keeps them informed of the progress of patients, and alerts them in the case of necessity. RESULTS: A set of hypothetical patients has been defined based on clinically possible cases in order to perform a complete scenario evaluation. The results that have been verified by psychiatrists suggest the utility of the platform. CONCLUSION: The proposed major depression monitoring system takes advantage of current technologies and facilitates more frequent follow-up of the progress of patients during their home stay after being diagnosed with depression by a psychiatrist.Peer ReviewedPostprint (author's final draft

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Fuzzy Inductive Reasoning (FIR) is a qualitative inductive modeling and simulation methodology for dealing with complex dynamical systems. FIR has proven to be a powerful tool for qualitative model identification and prediction of future behavior of different kinds of system domains including biology, medicine, ecology, etc. FIR has been mainly applied to regression problems, but recently we are interested in studying the feasibility of FIR as a classifier. The main objective of this study is to analyze and revise the model selection process in FIR methodology from the perspective of a classifier when dealing with imbalance data. In this research we propose a wrapper technique for fuzzy model identification in the context of FIR. We demonstrate that this new approach exhibits a significant improvement comparing to classical FIR model selection when applied to imbalanced data classification. In this paper we also compare FIR Classifier with wrapper model selection to similar genre of classic rule-based and instance-based classifiers, i.e. RISE, kNN, C4.5, CN2, PART, RIPPER and Modlem, when applied to a set of classification benchmarks.