MedReg-KG: KnowledgeGraph for Streamlining Medical Device Regulatory Compliance

Abstract

Healthcare providers are deploying a large number of AI-driven Medical devices to help monitor and medicate patients. For patients with chronic ailments, like diabetes or gastric diseases, usage of these devices becomes part of their daily lifestyle. These medical devices often capture personally identifiable information (PII) and hence are strictly regulated by the Food and Drug Administration (FDA) to ensure the safety and efficacy of the medical device. Medical device regulations are currently available as large textual documents, called Code of Federal Regulations (CFR) Title 21, that cross-reference other documents and so require substantial human effort and cost to parse and comprehend. We have developed a semantically rich framework MedReg-KG to extract the knowledge from the rules and policies for Medical devices and translate it into a machine-processable format that can be reasoned over. By applying Deontic Logic over the policies, we are able to identify the permissions and prohibitions in the regulation policies. This framework was developed using AI/Knowledge extraction techniques and Semantic Web technologies like OWL/RDF and SPARQL. This paper presents our Ontology/Knowledge graph and the Deontic rules integrated into the design. We include the results of our validation against the dataset of Gastroenterology Urology devices and demonstrate the efficiency gained by using our system.