Find Similar Patient Journeys With Neo4j Aura Graph Analytics

The blog post explores using Neo4j Aura Graph Analytics to model and analyze patient journeys within the fragmented healthcare system, with a specific focus on patients with kidney disease. By employing graph database techniques, the analysis identifies similar care paths among patients using node similarity and communities through the Louvain method. The process involves setting up a Python notebook in Google Colab, converting patient IDs to a numeric format, and constructing a graph to perform pairwise comparisons of patients based on their treatment histories. This approach reveals patterns in patient care, which can help predict future procedures and tailor treatment plans. The blog provides a practical guide, including code snippets and instructions, for replicating the analysis, with resources available on GitHub for further exploration and application in different environments, such as Snowflake.