From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights.
Katrin Hänsel, Sarah N Dudgeon, Kei-Hoi Cheung, Thomas J S Durant, Wade L Schulz
Author Information
Katrin Hänsel: Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
Sarah N Dudgeon: Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
Kei-Hoi Cheung: Section of Biomedical Informatics, Department of Emergency Medicine, Yale School of Medicine, 55 Park Street, PS 210, New Haven, CT, 06510, USA.
Thomas J S Durant: Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
Wade L Schulz: Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA. wade.schulz@yale.edu.
Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.
