A Census of Disease Ontologies
For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as aetiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: (a) search, retrieval, and annotation of knowledge; (b) data integration and analysis; (c) clinical decision support; and (d) knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.
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Classification, Ontologies and Precision Medicine
A goal of precision medicine is to stratify patients in order to improve diagnosis and medical treatment. Translational investigators are bringing to bear ever greater amounts of heterogeneous clinical data and scientific information to create classification strategies that enable the matching of intervention to underlying mechanisms of disease in subgroups of patients. Ontologies are systematic representations of knowledge that can be used to integrate and analyze large amounts of heterogeneous data, allowing precise classification of a patient. In this review, we describe ontologies and their use in computational reasoning to support precise classification of patients for diagnosis, care management, and translational research.
(NOTE - some detail relating to SNOMED CT is outdated, but the overall view of the article is helpful)
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Interoperability between phenotypes in research and healthcare terminologies—Investigating partial mappings between HPO and SNOMED CT
Objectives: To investigate and contrast lexical and logical approaches to deriving partial mappings between HPO and SNOMED CT.
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Extending the coverage of phenotypes in SNOMED CT through post-coordination
Objective - To extend the coverage of phenotypes in SNOMED CT through post-coordination.