Life Sciences and Healthcare Use Cases with Knowledge Graphs

Ilian Uzunov and Doug Kimball from Ontotext talk about use cases in Life Sciences and Healthcare that can greatly benefit from knowledge graph solutions

Knowledge graphs can bring great value to Healthcare and Life Sciences organizations. Dive into this discussion between Ilian Uzunov, Life Sciences and Healthcare Director, and Doug Kimball, CMO, where they talk about their transformative impact in ingesting, managing and utilizing complex datasets, better analytics and insights generation, and improved efficiency and decision-making.

  • Complex data management – knowledge graphs act like data hubs that store data, metadata, and content, offering new perspectives on data management and consumption.
  • Enhanced data relationships – the power of knowledge graphs lies in their ability to trace relationships between data points tailored to specific business needs.
  • Data-driven decision-making – knowledge graphs facilitate better data sharing across an organization, increasing data literacy and collaboration. This improvement powers data-driven decision-making, which is essential for organizations dealing with large data volumes.
  • Applications in Pharma and Healthcare – there are various applications: from therapeutic target discovery and pre-clinical research to manufacturing and market access in the Pharma value chain to medical coding, Healthcare claims processing, and comprehensive data analytics views (for example, Patient 360) in Healthcare.
  • Profitability and efficiency – knowledge graphs contribute to more efficient research and analytics workflows, impacting market reach and cost optimization. They help companies bring products to market faster, mainly through tools developed for drug discovery processes, like Ontotext Target Discovery.
  • How to get started – organizations interested in adopting knowledge graphs are advised to start small, using existing vocabularies or terminologies as building blocks for their reference data layer. The initial focus should be on one or two use cases to prove value before expanding the knowledge graph to cover more areas.


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