McInnis Melvin G, Coleman Ben, Hurwitz Eric, Robinson Peter N, Williams Andrew E, Haendel Melissa A, McMurry Julie A
Department of Psychiatry, University of Michigan, Ann Arbor, Michigan.
Jackson Laboratory, University of Connecticut, Farmington, Connecticut.
Biol Psychiatry. 2025 Aug 15;98(4):293-301. doi: 10.1016/j.biopsych.2025.05.014. Epub 2025 May 23.
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.
本体是通过系统地定义概念、类别及其关系来表示知识的结构化框架。虽然本体在生物医学中得到了广泛应用，但在心理健康研究和临床护理中却基本不存在，该领域仍严重依赖现有的分类系统（如《精神疾病诊断与统计手册》）。尽管这些分类系统对临床交流和管理目的很有用，但它们缺乏整合不同数据源或支持人工智能分析所需的语义结构、计算属性和推理属性。对分类系统的这种依赖限制了对复杂、异质的精神病学数据进行分析和解释的努力。在情绪障碍，特别是双相情感障碍中，缺乏形式化的语义模型导致诊断不一致、数据结构碎片化以及精准医学的障碍。相比之下，本体为链接多模态数据源（如电子健康记录、基因和神经影像数据以及健康的社会决定因素）提供了一个标准化的、机器可读的基础，同时实现安全的、去识别化的计算。在这篇综述中，我们调查了心理健康本体的当前状况，并强调人类表型本体（HPO）是连接精神病学和医学表型的一个有前景的框架。我们描述了通过精心策划的精神病学术语、完善的定义以及对观察到的现象进行结构化映射来增强HPO的持续努力。全球双相情感障碍队列（GBC）是一项国际合作项目，通过开发一个针对双相情感障碍的、由共识驱动的本体来例证这种方法。通过支持语义互操作性、可重复研究和个性化护理，基于本体的方法为克服分类系统的局限性和推进数据驱动的精准精神病学提供了必不可少的基础设施。
