Ontology for Newborn Screening: Towards a Knowledge Graph for Sickle Cell Disease

Kpangni Alex Jérémie Koua, Bara Diop, Mamadou Diop, Cheikh Talibouya Diop, Lamine Diop

Abstract: Newborn screening (NBS) is a preventive public health practice aimed at detecting congenital and genetic disorders at an early stage, enabling timely interventions and improved management strategies. However, the heterogeneity of NBS data, including demographic, clinical, paraclinical, and laboratory information, poses major challenges to integration, interoperability, and automation. Ontologies offer a formal semantic framework to represent this knowledge consistently and to support advanced data exploitation through knowledge graphs. This paper proposes an ontology-based framework to structure and integrate heterogeneous NBS data. The approach models the screening process, defines domain-specific classes and properties, establishes hierarchical relations with constraints, and implements the ontology in OWL2. A case study on neonatal sickle cell disease (SCD) screening demonstrates its applicability. The main contributions include a semantic model of the neonatal screening process, an interoperable foundation for integrating diverse data sources, and the first steps toward building a neonatal screening knowledge graph. This work establishes a foundation for enhancing reasoning, decision support, and the future integration of AI into NBS programs, paving the way for more efficient targeted screening strategies.

External IDs:dblp:conf/bigdataconf/KouaDDDD25