Seed Grant Profile
Semantic Web Technology to Integrate Long Term Follow Up Newborn Screening Data
The development of guidelines for the long term clinical management of children with inherited disorders that are diagnosed through newborn screening (NBS) is hampered by the fact that these conditions are rare and comparatively little evidence-based data exists to evaluate available treatments. The evidence-based long term follow up (LTFU) data that does exist often spans multiple, geographically separated sources (e.g. state labs, primary, secondary, and tertiary hospitals, local practices, group practices and patient support groups). There also exists extreme variability among the various stake holders with regards to IT implementation, data modeling, gathering and storage, further complicating the development of evidence-based management guidelines. More simply, clinical knowledge is fragmented and isolated in silos. Proper data integration and aggregation is a basic requirement to answer ad hoc questions cutting across disparate systems and domains. In this project, we look at emerging Semantic Web technology to address data integration, using Phenylketonurea (PKU) as a model. To the best of our knowledge, this will be the first dedicated effort aimed at developing an integrated PKU ontology to overcome the barriers of retrieving fragmented information from disparate sources. Ultimately, this effort may serve as a model for addressing similar informatics-based challenges across other disorders identified in children through newborn screening.
Investigators: Rani Singh (Emory University School of Medicine), Prabhu Shankar (Emory University School of Medicine) and Shamkant Navathe (Georgia Tech)