With the explosion of healthcare information, there has been a tremendous amount of heterogeneous Textual Medical Knowledge (TMK), which plays an essential role in healthcare information systems. Knowledge graphs (KGs) enable better data representation and knowledge inference by arranging and incorporating the TMK into graphs. It automatically obtains knowledge from knowledge graphs with high precision, by focusing on taxonomy with individual health, their medications, brands, pricing, etc. To build a high quality and thorough clinical Knowledge Graph (KG), Spark NLP Relation Extraction (RE) Models and Neo4j Graph DB are used. Main aim is to provide a thorough taxonomy and a general view of healthcare KG construction It could provide insights into the patient’s history of medication, the results of various clinical tests, the efficacy of the treatment, and details about the drugs.
