MedReg-KG: KnowledgeGraph for Streamlining Medical Device Regulatory Compliance

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.

I. Introduction

Artificial Intelligence and machine learning (AI/ML) algorithms have recently gained a lot of attention for their ability to identify and learn patterns automatically from larger datasets. These technologies hold great potential to enhance the efficiency and precision of healthcare delivery, capitalizing on the latest advancements in big data [1], [2]. Following the digitization of healthcare systems, the extensive and continuous data generated during patient care is captured and stored as Electronic Health Record (EHR) data. According to the National Academy of Medicine, the essential functions of EHR include health information and data, decision support, electronic communication and connectivity, patient support, administrative processes, and reporting, as well as population health management [3]. In recent decades, the usage of machine learning (ML) and deep learning (DL) has significantly advanced various applications, including communicable disease diagnosis [4], resource allocation through task prediction [5], patient diagnosis [6]–[8], length-of-stay prediction [9], cancer diagnosis, mortality estimation [10] from EHR data, medical images [11] [12]. Knowledge Graphs have been widely adopted to enhance data insights and complement EHR modeling.