Affiliations: [a] Computer Science Department, Universitat Politècnica de Catalunya, Barcelona, Spain | [b] Parc Sanitari Sant Joan de Déu, Sant Boi del Llobregat, Spain
Abstract: BACKGROUND 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.
