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Accurate classification of patients would enable precision medicine to improve medical diagnosis and treatment. The availability of a vast amount of clinical data and scientific information makes it difficult for a physician to organize and analyze this data and improve patients' classification. This challenge can be overcome by using ontologies to enrich computational reasoning.

There are vast amounts of subjective and objective observations related to demographic characteristics, findings, symptoms, diagnoses, medications, among others, that are available in the electronic health records. Ontologies could organize and analyze this large amount of data and enhance computational reasoning to support patients' accurate classification for diagnosis, care management, and translational research.

The Human Phenotype Ontology (HPO) is one of the outstanding examples of ontologies enhancing patients' classification. It relates the computable phenotypic profiles of human diseases and individual patients with terms that are close to each other in the hierarchy and weighted according to the specificity of unique phenotypic abnormalities. HPO provides a more detailed representation of clinical phenotypes than other clinical terminologies or ontologies and is designed for computational analysis. Furthermore, this method represents the patient as a biological subject.

It would not be an exaggeration to believe that ontologies are slowly but surely transforming the wish to use the vast amount of data available to classify patients accurately and identify effective and safe treatments into a reality. The National Research Council forum on precision medicine has proposed a new taxonomy for biology and medicine that would be structured to recognize and avail the multiple axes of basic science and clinical characteristics as a matrix defining disease endotypes stands testimony to this fact.

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