How Neo4j Aura Graph Analytics Helps Developers Model Real-World Patient Data

In this video, learn how Neo4j Aura Graph Analytics empowers data analysts to model and analyze real-world patient data for more personalized healthcare solutions. We’ll walk through loading patient and procedure data, constructing a graph, and applying node similarity and community detection algorithms. Whether you’re building healthcare applications or exploring graph analytics for the first time, this session will show you how to uncover hidden patterns, identify similar patients, and tailor care pathways using Neo4j’s cloud-native tools.

What You’ll Learn:
– Setting up and cleaning healthcare data for graph modeling
– Using Jaccard-based node similarity to find related patients
– Applying the Louvain algorithm to detect patient communities
– Building personalized treatment plans from graph insights

Explore Neo4j Aura Graph Analytics → https://bit.ly/4mCQKQQ
Learn more about Neo4j AuraDB → https://bit.ly/451ssda
Access the code and dataset on GitHub → https://bit.ly/3FvQuCC