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

In this video, learn how Graph Analytics for Snowflake empowers data analysts to model and analyze real-world patient data for more personalized healthcare solutions. You’ll learn how to seamlessly integrate synthetic patient data into Snowflake, configure the Neo4j Graph Analytics App, and harness advanced algorithms like the Jaccard coefficient for precise pairwise similarity scoring. All natively within Snowflake with no ETL.

What You’ll Learn:
How to model healthcare data in Snowflake as a graph with patient and procedure nodes

How to compute pairwise patient similarity using the Jaccard coefficient via the Neo4j Graph Analytics for Snowflake App

How to run Louvain community detection in Snowflake to uncover patient cohorts

How to leverage similarity scores and communities to predict and prioritize patient interventions

Explore Neo4j Graph Analytics for Snowflake → https://www.youtube.com/playlist?list=PL9Hl4pk2FsvVZf8IYFwXy1Rlw9A0q11Wl
Learn more about Neo4j AuraDB → https://bit.ly/451ssda
Access the code and dataset on GitHub → https://bit.ly/468kBuT

#GraphAnalytics #Neo4j #PatientJourney #Snowflake #MachineLearning #GDS #FactoryOps #DataScience #GraphAlgorithms