Using Knowledge Graphs to Understand Real-World Data and Identify Stakeholders

As more companies are utilizing real-world data (RWD) and real-world evidence (RWE) for drug development and market access, new strategies are needed to sift through massive data pools to identify actionable insights. In a new IQVIA blog post, learn about how a research team used artificial intelligence and knowledge graphs to identify key stakeholders in digital health policy data.

According to Jane Reed, “Search criteria were set by the regulatory and RWD experts, in collaboration with the pharma researchers. Once search criteria were set, the NLP team built and trained AI algorithms on what to search for, where to look, and how to dig deeper based on citations and references within the text. The NLP team used their platform to draw structured meaning from the data based on context, frequency, and connections between stakeholders. The team standardized the output using defined ontologies, then passed the results to the Knowledge Graph team who ingested the NLP output into a graph model.  Having the data modelled in a knowledge graph enabled visualization of the entities and their relationships to graphically depict the key-opinion-leader landscape as well as providing a basis to apply graph analytical algorithms to score the importance of documents and stakeholders based on their links to other documents and stakeholders.”

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(Source: IQVIA, June 19^th, 2023)