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Electronic Health Records are electronic data generated during or as abyproduct of routine patient care. Structured, semi-structured and unstructuredEHR offer researchers unprecedented phenotypic breadth and depth and have thepotential to accelerate the development of precision medicine approaches atscale. A main EHR use-case is defining phenotyping algorithms that identifydisease status, onset and severity. Phenotyping algorithms utilize diagnoses,prescriptions, laboratory tests, symptoms and other elements in order toidentify patients with or without a specific trait. No common standardized,structured, computable format exists for storing phenotyping algorithms. Themajority of algorithms are stored as human-readable descriptive text documentsmaking their translation to code challenging due to their inherent complexityand hinders their sharing and re-use across the community. In this paper, weevaluate the two key Semantic Web Technologies, the Web Ontology Language andthe Resource Description Framework, for enabling computable representations ofEHR-driven phenotyping algorithms.

Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms