ONTOPHARMX: FEDERATED ONTOLOGY-DRIVEN SEMANTIC FRAMEWORK FOR SECURE AND INTEROPERABLE BIOMEDICAL DATA INTEGRATION

Abstract

Integrating biomedical information in healthcare organizations continues to be a challenge because of lack of cohesion, semantic variations, and tight privacy controls including the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). Such limitations affect the collaboration analytics and hinder the move towards precision medicine. In this paper, we introduce OntoPharmX, a federated ontology-based semantic model, which can allow the integration of biomedical data with security, interoperability, and privacy through semantic networks. The suggested framework integrates semantic harmonization and HL7-FHIR adapters based on the OWL/RDF with cross-silo federated learning using secure aggregation and differential privacy to protect the privacy of patients without harming the analytical performance. OntoPharmX employs a standardized data transformation pipeline based on the modular pipeline of transforming ETL to FHIR to RDF and uses Flower/TensorFlow Federated to coordinate decentralized models and then an SPARQL-based reasoning. OntoPharmX has been experimentally validated across distributed healthcare locations to show that it can achieve near-centralized model accuracy and provide semantic alignment and can trade-off privacy and utility quantitatively. With the system, cohort discovery is privacy preserving and cross-institutional is achievable without undermining compliance or data sovereignty.