MedQGraph: TMKGs for AI-Driven Healthcare Insights

Session Track: Knowledge Graphs

Session Time: 09:00 pm - 09:15 pm November 6

Session description

Clinical decision-making depends on Electronic Health Records (EHRs), but their complexity hinders efficient extraction and analysis. Traditional knowledge graph methods focus mainly on structured data and static relationships, limiting advanced querying. We present the Medical Record Knowledge Graph (MRKG), a scalable framework that transforms structured MIMIC-IV EHR data into an interpretable, queryable graph using Neo4j. MRKG integrates diverse clinical entities, diagnoses, procedures, and medications into a cohesive structure, enabling comprehensive exploration of patient history.

Speakers

Isaac Ritharson

AI Researcher, Northeastern University

Isaac Ritharson is a dedicated and accomplished AI researcher at Northeastern University (NU) in Seattle, WA, who is passionate about positively impacting society. With expertise in applied artificial intelligence, he explores the intricate connection between neural networks and the biological aspects of our world. Isaac's work is driven by a deep commitment to addressing real-world challenges and providing tangible solutions. Beyond his researcher role, he is actively involved in advocacy for children's rights and social causes, and is currently the president of the NU Lead organization. Isaac's multifaceted approach combines his technical proficiency with a compassionate mindset, making him an invaluable asset in leveraging AI for the betterment of humanity.

Ishan Chaudhary

Graduate Research Student, Northeastern University

Ishan Chaudhary is a graduate research student currently pursuing a master’s degree in Data Science at Northeastern University. His research focuses on the application of artificial intelligence in healthcare, with a particular emphasis on how AI advancements can enhance clinical outcomes and transform patient care. He is deeply interested in bridging the gap between people and technology, aiming to make AI-driven solutions more accessible and impactful in real-world healthcare settings.