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Integrating Knowledge: The Power of Ontologies in Psychiatric Research and Clinical Informatics
Melvin G McInnis1, Ben Coleman2, Eric Hurwitz3
1Department of Psychiatry, University of Michigan, Ann Arbor, Michigan.
Abstract:
Ontologies are structured frameworks for representing knowledge by systematically defining concepts, categories, and their relationships. While widely adopted in biomedicine, ontologies remain largely absent in mental health research and clinical care, where the field continues to rely heavily on existing classification systems (e.g., the DSM). Although useful for clinical communication and administrative purposes, they lack the semantic structure, computational properties, and reasoning properties needed to integrate diverse data sources or support artificial intelligence-enabled analysis. This reliance on classification systems limits efforts to analyze and interpret complex, heterogeneous psychiatric data. In mood disorders, particularly bipolar disorder, the lack of formalized semantic models contributes to diagnostic inconsistencies, fragmented data structures, and barriers to precision medicine. By contrast, ontologies provide a standardized, machine-readable foundation for linking multimodal data sources, such as electronic health records, genetic and neuroimaging data, and social determinants of health, while enabling secure, deidentified computation. In this review, we survey the current landscape of mental health ontologies and highlight the Human Phenotype Ontology (HPO) as a promising framework for bridging psychiatric and medical phenotypes. We describe ongoing efforts to enhance the HPO through curated psychiatric terms, refined definitions, and structured mappings of observed phenomena. The Global Bipolar Cohort (GBC), an international collaboration, exemplifies this approach through the development of a consensus-driven ontology tailored to bipolar disorder. By supporting semantic interoperability, reproducible research, and individualized care, ontology-based approaches provide essential infrastructure for overcoming the limitations of classification systems and advancing data-driven precision psychiatry.
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