The GALENOS Upper-Level Mental Health Ontology: Enabling evidence synthesis by representing the domain of mental health and associated interventions

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This paper describes the GALENOS Upper-Level Mental Health Ontology, which aims to represent mental health domains and associated interventions in a structured way to enable evidence synthesis. The authors present an ontology designed for capturing concepts and relationships relevant to mental health research and interventions, focusing on representing the domain at a level suitable for downstream synthesis. A key limitation is that the provided text is largely comprised of embedded web/script code rather than the paper’s scientific methods, results, and stated constraints, making the ontology’s empirical performance and validation details impossible to verify from the excerpt alone. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Background Synthesising mental health evidence is challenging due to fragmentation of the field, a high volume of publications and inconsistent terminology being used. Without a better overview of the evidence, developing evidence-based interventions to improve mental health is challenging. The Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) addresses these challenges by synthesising and regularly updating evidence through living systematic reviews on anxiety, depression and psychosis. This project is developing a precise classification framework, an ontology, to organise the evidence from these reviews in a data repository. This paper presents the ontology’s upper-level structure, which specifies, defines and links the classes that will organise the data identified in systematic reviews into a shared framework with a common language. Methods The development of the upper-level ontology followed five steps: (1) specifying the ontology’s scope; (2) drafting classes and relationships by reusing parts of existing ontologies and creating new classes where necessary; (3) performing a stakeholder consultation incorporating feedback from mental health experts and people with lived experience; (4) specifying relationships between classes; and (5) making the ontology machine readable and publishing it online. Results The ontology’s upper level includes 25 key classes covering research studies, human populations (including population history, persons and their personal histories), mental health interventions (encompassing both content and delivery) and intervention scenarios, which represent that interventions are delivered in specific contexts with varying plans, outcomes, mechanisms of action and levels of participant engagement. The classes are connected by nine relationship types such as participates_in and has_part. Conclusion The upper-level ontology provides a structured framework to represent key aspects of mental health and mental health interventions, supporting linking data and integrating evidence. It also lays the basis for a more elaborated ontology of mental health and interventions.
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