Powering Diverse Clinical Applications through Elsevier’s Healthcare Knowledge Graph

Knowledge Graphs are increasingly being developed and leveraged in academia and industry to tackle complex biomedical challenges, such as drug discovery and safety, medical literature search, clinical knowledge management, and disease monitoring. In this talk, we will present the research and development on Elsevier’s Healthcare Knowledge Graph, a platform built to power advanced clinical decision support and enhanced point-of-care content discovery for clinicians and patients. Elsevier’s Healthcare Knowledge Graph uses linked data and semantic web technologies to capture knowledge and data from heterogeneous healthcare sources about diseases, drugs, findings, guidelines, cohorts, journals, and books. Subject matter experts regularly curate and visualize the Healthcare Knowledge Graph through novel exploration interfaces to keep the medical knowledge regularly updated. Moreover, operationalized natural language processing and machine learning pipelines continuously tag and extract novel medical concepts and relations within medical content. Elsevier’s Healthcare Knowledge Graph platform is used to deliver actionable knowledge in user-facing applications in focused and precise clinical search, clinical decision support, and content recommendation. This talk will provide a perspective on how knowledge graphs enable the capture, representation, and provision of complex medical knowledge of high velocity, variety, volume, and veracity, to power, trusted clinical and biomedical research applications.