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Healthcare systems play an important role in the well-being of patients; however, the diagnostic process generates a very large and varied types of data which makes the process of analyzing this data very complicated. More precisely, depression, which is one of the most common psychological disorders, contains a taxonomy of different symptoms, heterogeneous, and varied by data criteria, as confronted by clinicians to predict the degree of the disorder in patients with the aim of selecting the best treatment. To this end, the authors propose a decision architecture based on an approach that combines method ontologies, the Analytic Hierarchy Process, in the context of the prevention and monitoring of depression trends in patients.