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Enhancing Benefit Adjudication through Graph Node Embedding, Clustering, and Outlier Detection

Session Track: Data Intelligence

Session Time:

Session description

In this presentation, we show how a benefit adjudication process can be improved with graph data science techniques. Our goal is to capture complexities and to reveal patterns and nuances in data sets that would otherwise be difficult using conventional analysis techniques. We demonstrate how graph node embeddings combined with clustering can effectively detect outliers in a realistic patient network. Our program includes modeling hospitalization and benefit data, ingestion into a Neo4j graph database, generating node embeddings, finding similarity among patients, and using similarity to identify outliers. We construct a synthetic graph populated with realistic adjudication data, which we use to evaluate two node embedding algorithms—node2vec and GraphSAGE. These algorithms are chosen because they emphasize different features that need to be incorporated into the two models, allowing us to demonstrate their contrasting strengths and limitations. With the resulting embeddings, we perform clustering analysis to identify outliers in the graph.

Speakers

photo of Manish Mithaiwala

Manish Mithaiwala

Senior Graph Data Scientist, Amida Technology Solutions

Manish Mithaiwala is a seasoned data scientist and engineer with over a decade of experience in designing and deploying scalable data solutions that drive business impact. As a senior graph data scientist at Amida Technology Solutions, Manish brings expertise in graph data science and knowledge graph construction to deliver cutting-edge analytics solutions. With a strong technical foundation in algorithm development, cloud computing, and data engineering, Manish is well-versed in leveraging advanced data technologies to extract insights and drive business decisions. He is a skilled collaborator and communicator, with a passion for using data to drive strategic growth and innovation. Throughout his career, Manish has successfully applied his expertise to tackle complex data challenges.

photo of Yao Ma

Yao Ma

Assistant Professor, Rensselaer Polytechnic Institute

Yao Ma is an assistant professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). Before joining RPI, he worked as an assistant professor at New Jersey Institute of Technology. Yao got his Ph.D. from Michigan State University in 2021 under the supervision of Dr. Jiliang Tang. Before that, he completed his master's degree (2016) in Statistics, Probability and Operations Research at Eindhoven University of Technology and his bachelor's degree (2015) in Mathematics and Applied Mathematics at Zhejiang University.