Clinical Knowledge Representation in Data Science.

The vast potential of observational healthcare data in biomedical discovery remains largely unrealized because clinical records are fragmented, unstructured, and generated for patient care rather than research. Clinical knowledge representation (KR) helps to bridge this gap by encoding information in standardized, computable formats that preserve meaning and context. This review examines KR across the clinical data life cycle, from the generation of data in healthcare settings to their transformation for secondary use and eventual application in data science. We highlight foundational components such as standardized terminologies, ontologies, and common data models that enable data harmonization and interoperability. We further discuss how these structured representations support multimodal data integration and the development of more accurate, interpretable artificial intelligence models. Adopting a semantics-first approach to KR is essential for transforming fragmented clinical data into reusable, trustworthy knowledge that advances data-driven discovery and improves patient care.