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 and present them in a structured online data repository. Aim This study aims to develop an ontology of mental health that includes entities with clear labels and definitions to describe and synthesise evidence about mental health, focusing on anxiety, depression and psychosis. Methods We will develop and apply the GALENOS Mental Health Ontology through eight steps: (1) defining the ontology’s scope; (2) identifying, labelling and defining the ontology’s entities for the GALENOS living systematic reviews; (3) structuring the ontology’s upper level (4) refining entities via iterative stakeholder consultations regarding the ontology’s clarity and scope; (5) formally specifying the relationships between entities in the Mental Health Ontology; (6) making the ontology machine-readable and available online; (7) integrating the ontology into the data repository; and (8) exploring the ontology-structured repository’s usability. Conclusion and discussion The Mental Health Ontology supports the formal representation of complex upper-level entities within mental health and their relationships. It will enable more explicit and precise communication and evidence synthesis about anxiety, depression and psychosis across the GALENOS Project’s living systematic reviews. By being computer readable, the ontology can also be harnessed within algorithms that support automated categorising, linking, retrieving and synthesising evidence.
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