title: A model for continuous monitoring of patients with major depression in short and long term periods.
abstract: 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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Computer Science Department, Universitat Politècnica de Catalunya, Barcelona, Spain.
authors.forename: Francisco
authors.initials: F
authors.lastname: Mugica
authors.affiliation: Computer Science Department, Universitat Politècnica de Catalunya, Barcelona, Spain.
authors.forename: Àngela
authors.initials: À
authors.lastname: Nebot
authors.affiliation: Computer Science Department, Universitat Politècnica de Catalunya, Barcelona, Spain.
authors.forename: Solmaz
authors.initials: S
authors.lastname: Bagherpour
authors.affiliation: Parc Sanitari Sant Joan de Déu, Sant Boi del Llobregat, Spain.
authors.forename: Luisa
authors.initials: L
authors.lastname: Baladón
authors.affiliation: Parc Sanitari Sant Joan de Déu, Sant Boi del Llobregat, Spain.
authors.forename: Antonio
authors.initials: A
authors.lastname: Serrano-Blanco
mesh_terms: D003142:Communication; D003199:Computer Systems; D003661:Decision Support Techniques; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D008962:Models, Theoretical; D011569:Psychiatric Status Rating Scales; D058998:Remote Sensing Technology / Q000379:methods; D013997:Time Factors
keywords: Major depressive disorder; qualitative reasoning; remote monitoring system; rule-base system