Hepdyslip: Merged Ontologies For Hepatitis And Dyslipidaemia

Abstract

EHR systems has transformed healthcare data management but challenges related to interoperability, cost and security remain significant. Role of ontologies in EHR for representing the unstructured and semi-structured data found in different medical records. such as doctor's prescription, diagnostic report, and other treatment-related data and different biomedical knowledge bases are well established. The ontologies and knowledge graphs are used for representing and structuring the unstructured data so that it can be used for coding and retrieving the relevant data in knowledge bases. Knowledge Graphs are flexible and adaptable as it organizes data into interconnected structures that represent relationships between entities such as disease, treatments, and progressions. Implied relationship between many diseases is known however they involve complex interdependencies. Ontology merging plays an important role in showing the causal relationship among such diseases. The objective of ontology merging is to integrate pertinent features of ontologies, like axioms, persons, and annotations, into the resultant ontologies that can be used for better establishing the semantic relationship between two diseases. In this study two diseases considered are Hepatitis and Dyslipidemia and converging their ontologies a HypDysLip ontology is created. The created ontology is further enriched using the generative AI tool ChatGPT.