Inspiration
The rapid advancements in healthcare data analytics inspired us to develop Synthea Healthcare Graph Analytics. We wanted to create a system that utilizes graph-based techniques to uncover hidden insights from synthetic patient records, helping healthcare professionals make informed decisions. The idea stemmed from the increasing need to analyze patient interactions, identify high-risk individuals, and simulate disease spread efficiently.
What it does
Synthea Healthcare Graph Analytics is a powerful tool designed to:
- Convert patient records and encounters into a graph structure.
- Provide interactive visualization of patient interactions using PyVis.
- Use PageRank to identify high-risk individuals based on healthcare encounters.
- Simulate disease spread over patient networks to predict potential outbreaks.
- Offer a real-time dashboard using Streamlit to explore insights and detect anomalies.
How we built it
Dataset Integration:
- We used Synthea Synthetic Patient Records, including
patients.csvandencounters.csv. - Data was loaded using pandas and processed for graph construction.
- We used Synthea Synthetic Patient Records, including
Graph Construction:
- Built a NetworkX graph with patients as nodes and encounters as edges.
- Weighted edges based on frequency and duration of patient interactions.
Graph Analytics:
- Applied PageRank to rank patients based on their influence within the network.
- Implemented a disease spread model to simulate infections over patient encounters.
Visualization & Dashboard:
- Used PyVis for interactive graph visualization.
- Developed a Streamlit dashboard to provide real-time insights into patient risk levels.
Challenges we ran into
- Data Cleaning & Preprocessing: Handling missing or inconsistent data from synthetic patient records.
- Graph Scalability: Managing large patient datasets while ensuring efficient graph processing.
- Visualization Performance: Optimizing PyVis rendering for large-scale graphs.
- Disease Spread Simulation: Fine-tuning infection probability to reflect realistic scenarios.
Accomplishments that we're proud of
- Successfully transformed synthetic healthcare data into an interactive graph model.
- Developed a real-time dashboard that enables dynamic patient risk analysis.
- Created an efficient disease spread simulation to predict potential outbreaks.
- Ensured scalability of graph processing using NetworkX and PyVis.
What we learned
- The power of graph analytics in modeling complex healthcare networks.
- Techniques for visualizing healthcare interactions using PyVis.
- The importance of data preprocessing in ensuring accurate analysis.
- How to optimize Streamlit dashboards for real-time interactive analytics.
What's next for Synthea Healthcare Graph Analytics
- Integration with Real Hospital Data: Enhancing the model with real-world patient interactions.
- Machine Learning Models: Improving patient risk prediction using AI-based techniques.
- Dynamic Graph Updates: Enabling real-time updates as new patient records are added.
- Enhanced Disease Spread Models: Incorporating additional parameters for more accurate predictions.
- Advanced User Analytics: Providing role-based access for different healthcare professionals.
🚀 Empowering Healthcare with Graph Analytics!
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