JMIR Publications

ABSTRACT

Background:

Psychiatric diagnostic practice faces persistent challenges in adequately capturing the complexity, heterogeneity, and dynamic nature of mental disorders. Conventional categorical classification systems such as DSM-5 and ICD-11 rely on rigid symptom-based criteria that often fail to represent multifactorial etiologies, dimensional symptom profiles, and high rates of comorbidity. Although dimensional models, domain-specific ontologies, and alternative taxonomies have been developed to address these limitations, they are typically applied in isolation, resulting in fragmented knowledge representations and limited interoperability. This lack of semantic integration hampers data harmonization, cross-domain analysis, and the advancement of personalized psychiatry.

Objective:

The objective of this work is to develop and present the Meta Mesh Ontology (MMO) and its Core Domain Framework (CDF) as an integrative approach for harmonizing heterogeneous psychiatric knowledge bases and enabling personalized, multidimensional diagnostic representations.

Methods:

We designed a modular ontology architecture grounded in the Basic Formal Ontology to ensure structural and semantic consistency across biological, psychological, and social domains. Existing psychiatric frameworks and ontologies—including dimensional models, diagnostic taxonomies, and socio-environmental knowledge systems—were systematically analyzed and integrated using standardized ontology engineering principles. The MMO was implemented using formal semantic web standards, including the Web Ontology Language and the Resource Description Framework, and aligned with established biomedical ontologies and informatics standards to support interoperability, extensibility, and machine-readable reasoning across domains. Evaluation used 2 MMO/CDF metric sets and 10 module metric sets to quantify ontology size, semantic alignments, external integration, and dataset readiness.

Results:

The Meta Mesh Ontology provides a unified semantic framework that enables the integration of heterogeneous psychiatric knowledge sources within a coherent structure. Its modular design supports flexible incorporation of domain-specific sub-ontologies while maintaining global consistency. MMO/CDF comprised 2,238 triples, 208 classes, 28 properties, 3 individuals, and 103 alignments (100 external) across 5 frameworks. The 10 modules added 967 triples and 171 alignments (116 external), with 21 integrations and complete label coverage (54/54 resources). The MMO facilitates cross-domain reasoning across biological, psychological, social, and environmental factors and supports the integration of multimodal data, including clinical records, environmental indicators, and sensor-based data streams. By semantically bridging dimensional frameworks such as the Hierarchical Taxonomy of Psychopathology (HiTOP) and the Research Domain Criteria (RDoC), the MMO functions as a mediator between complementary diagnostic paradigms and enables harmonized data representation across research and clinical contexts.

Conclusions:

The Meta Mesh Ontology establishes a scalable and interoperable foundation for integrating diverse psychiatric knowledge systems and data modalities. By enabling semantic harmonization and cross-domain reasoning, the MMO supports personalized, multidimensional psychiatric diagnostics that transcend rigid categorical classifications. This framework provides infrastructure for multicenter research, data-driven mental health modeling, and the translation of complex psychiatric knowledge into clinical decision support and personalized medicine.