Abstract - Development of a Graph Model for the OMOP Common Data Model
Current phenotyping and systems biology research requires not only integration of large volumes of Electronic Health Record (EHR) and multi-omics data, but also capturing the multitudes of relations among the concepts. Graph databases have emerged as a promising technology for such tasks, supporting not only local analysis but also global analysis leveraging graph algorithms like Centrality, Community Detection, Path Finding or Node Embeddings.Unfortunately, EHR data is rarely available in a graph format. While a nave row-to-node conversion is possible, the resulting graph is typically attribute-heavy, resulting in suboptimal performance. To address this limitation, we developed a modelling method to convert data form the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) to the Neo4j [www.neo4j.com] graph property model.
Graph Database, SCRIPT study, OMOP CDM
Kang, Mengjia, Alvarado-Guzman, Jose A., Rasmussen, Luke, Starren, Justin B
DigitalHub. Galter Health Sciences Library & Learning Center
Scoping Review of Neuromonitoring practices for neonates with congenital heart disease
Protocol for Scoping Review of Neuromonitoring in Neonates with Congenital Heart Disease
We seek to synthesize the known literature for neuromonitoring with EEG, aEEG, NIRS, transcranial doppler (TCD) and other multimodal neuromonitoring techniques for neonates with congenital heart disease to clarify current practices, document available studies, investigate gaps in research that may inform the care of this population.