What are the use cases for knowledge graphs in healthcare?

Knowledge graphs are used in healthcare to connect and organize complex data, enabling better decision-making and insights. They model relationships between entities like patients, diseases, treatments, and medical devices, making it easier to query interconnected data. Below are three key use cases.

1. Patient Data Integration and Personalization Healthcare systems often store data in silos—electronic health records (EHRs), lab results, imaging systems, and wearable devices. A knowledge graph can unify these sources by mapping relationships between patient profiles, diagnoses, medications, and genetic data. For example, a graph could link a patient’s allergy records to their prescribed medications, flagging potential adverse interactions. Developers might use RDF (Resource Description Framework) or property graph models (e.g., Neo4j) to represent this data. By querying the graph, care teams can quickly retrieve a holistic patient history, improving personalized treatment plans.

2. Clinical Decision Support Knowledge graphs can encode medical guidelines, research findings, and institutional protocols to assist clinicians. For instance, a graph might model relationships between symptoms, lab values, and recommended treatments for diabetes. When a patient’s data (e.g., blood glucose levels) is added, traversal algorithms could suggest insulin adjustments or screenings based on guidelines like those from the American Diabetes Association. Tools like FHIR (Fast Healthcare Interoperability Resources) standards can help structure input data, while graph queries (e.g., Cypher or SPARQL) generate actionable insights. This reduces manual effort and ensures adherence to evidence-based practices.

3. Medical Research and Drug Discovery Researchers use knowledge graphs to analyze connections between genes, proteins, diseases, and drugs. For example, a graph could reveal that a protein linked to Alzheimer’s also interacts with a cancer drug’s target, suggesting potential for drug repurposing. Public datasets like PubMed or DrugBank are often integrated into these graphs. Developers might build pipelines to ingest and map data using frameworks like Apache Jena, then apply graph algorithms (e.g., centrality analysis) to identify key nodes. This accelerates hypothesis generation and reduces trial-and-error in lab experiments.

In each case, knowledge graphs address healthcare’s data complexity by providing a flexible, queryable structure. Developers can leverage existing tools (e.g., graph databases, FHIR APIs) and focus on modeling domain-specific relationships to solve real-world problems.