Developing the Mental Health Ontology: Protocol for a step-wise method to develop an ontology for the mental health domain as part of the GALENOS Project

Abstract

Background Research about anxiety, depression and psychosis and their treatments is often reported using inconsistent language, and different aspects of the overall research may be conducted in separate silos. This leads to challenges in evidence synthesis and slows down the development of more effective interventions to prevent and treat these conditions. To address these challenges, the Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) Project is conducting a series of living systematic reviews about anxiety, depression and psychosis. An ontology (a classification and specification framework) for the domain of mental health is being created to organise and synthesise evidence within these reviews. It will also be an aid to synthesising evidence in the wider mental health field. Aim The aim of the study is to develop a Mental Health Ontology that includes entities with clear and unique labels and definitions to describe and synthesise evidence about mental health. Methods We will develop the Mental Health Ontology through six steps: (1) defining the ontology’s scope; (2) identifying, labelling and defining the ontology’s entities for the GALENOS living systematic reviews; (3) identifying and refining entities and their structure by drawing on existing classification frameworks; (4) refining entities via iterative stakeholder consultations regarding the ontology’s clarity and comprehensiveness; (5) formally specifying the relationships between entities in the Mental Health Ontology; and (6) making the ontology machine-readable and available online. Conclusion and discussion The Mental Health Ontology supports the formal representation of complex entities within mental health and their relationships. It will enable more explicit and precise communication about mental health across research groups and disciplines, and evidence synthesis across different sources. By being computer readable, the ontology can also be harnessed within algorithms that support automated categorising, retrieving and synthesising evidence.