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Easier than Excel: Social Network Analysis of
DocGraph with Gephi Janos G. Hajagos
Stony Brook School of Medicine
Fred Trotter fredtrotter.com
DocGraph Based on FOIA request to CMS by Fred Trotter Pre-released at Strata RX 2012 Medicare providers (more than doctors) CY 2011 dates of service Share 11 or more patients in a 30 day forward window Initial access restricted to MedStartr funders
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DocGraph by the numbers Directed graph Average total degree 52.8 940,492 providers (graph nodes/vertices) 49,685,810 shared edges
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Geographic visualization
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http://isurfsoftware.com/blog/2012/12/13/visualizing-geographic-connections-between-us-doctors/
DocGraph data
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NPPES National Plan and Provider Enumeration System Source of NPI (National Provider Identifier) No cost download Information is entered and updated by provider
- Data quality is good to poor CSV file with 314 columns A custom MySQL load script is used to normalize the database Bloom.api open source project to make data easier to access
- http://www.bloomapi.com/
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Tabular data
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Things we can do with tabular data
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Graph data Relation between authors and MeSH terms from PubMed
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http://dx.doi.org/10.6084/m9.figshare.94595
Graph types Undirected graph
- Facebook friendships Directed graph
- Twitter: follow and be followed Bipartite graph Multipartite
- RDF graph model - Property graph model Allow parallel edges
- RDF graph Model
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Components of a network/graph
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Graphs in healthcare Prescriber and patient (bipartite)
- NCPDP data with NPI Referral data sets Shared patients
- DocGraph Social networks
- Tweeting about a disease Limited by imagination
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Generating GraphML XML based file format for graphs Readable by a large number of tools
- Gephi - Mathematica - igraph (R) NetworkX a Python library for graphs which can export to GraphML GraphML is not a file format for really large graphs GraphML is not readable by d3.js
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GraphML can be loaded into Mathematica
Gephi
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Gephi Java based open source tool Focused on interactivity
- Fast graphics - Multi-threaded - Visual updates Strong graph analytics Graphs stored in memory
- Upper limit is about 100,000 nodes Netbeans plugin architecture
- Integration with Neo4J - Additional layout algorithms
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Downloading sample files
https://dl.dropboxusercontent.com/u/21690634/DocGraph/docgraph_tutorial_examples.zip
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Subsets are generated using a Python script
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python extract_providers_to_graphml.py "npi='1750499653'" sterrence Leaf-edges
Opening connection referral Configuration Selection criteria for subset graph: npi='1750499653' Referral table _name: referral.referral2011 NPI detail table name: referral.npi_summary_primary_taxonomy Nodes will be labeled by: provider_name Leaf-to-leaf edges will be exported? False … Imported 1 nodes … Imported 986 nodes … Imported 1724 edges Edge types imported {'core-to-leaf': 866, 'leaf-to-core': 856: None : 2} Leaf-to-leaf edges were not selected for export Writing GraphML file
Generating a subset: some concepts
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Core nodes
Adding leaf nodes
Connecting core nodes
Connecting to leaf nodes
Connecting leaf nodes
Sample files jamestown_core_provider_graph.graphml
- Providers selected with practice addresses in Jamestown, NY - Small city in far western New York (approximately 30,000 residents) - 179 nodes with 5,560 edges jamestown_core_and_leaf_provider_graph.graphml
- Includes providers above and those who are linked to them - 1,322 nodes with 12,457 edges albany_core_provider_graph.graphml
- Providers selected with practice addresses in Albany, NY - A small city in New York (approximately 100,000 residents) - 1,368 nodes with 44,711 edges
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Sample files (continued) bronx_core_provider_graph.graphml
- Providers selected with practice addresses in Bronx, NY - Urban community (1.4 million residents) - 3,268 nodes and 53,828 edges
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Opening a graph file
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Import report
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Force directed layout of the graph
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Results of the layout
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ForceAtlas 2 works well for larger graphs
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Navigating the graph Best experience with a three button mouse with a scroll wheel
- Right click and hold to pan - Scroll wheel to zoom in and out - Left click to select - Right click for context menus MacBook users
- command key and click and hold down on trackpad to pan - Two fingers to zoom on trackpad - Click on trackpad to select - Control click for context menus
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Coloring the graph (partitioning)
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Coloring the graph (partitioning)
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Varying node size based on importance Step 1: Need to select a measure for node importance
- Degree - PageRank - Eigenvector centrality Step 2: Run the measure against the graph Step 3: Ranking tab and “Size/Weight” Step 4: Set size range
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Graph measures Degree
- In-degree - Out-degree Graph structure measures
- Clustering (global and local) - Network diameter Centrality Measures
- Eigenvector centrality - PageRank (Google search) Community measures And more . . . . .
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Interactively viewing node attributes
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Click the “T” icon on the bottom to turn on node labeling
Data Laboratory
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Selecting visible fields
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Viewing edge attributes
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Saving your graph Save your graph in .gephi format
- xml based format - preserves layout, size, and color Save in GraphML format for use with outside programs
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Filtering nodes by attributes
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Hints for filtering nodes Drag field filter “is_physician” from the top pane to the lower pane Set the value to filter on
- Value should equal 1 - 1 is equivalent to true Click “Filter” to apply
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Producing a final graph
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We need to rescale the edge weights in the graph
Producing a final graph after scaling
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Bronx core provider graph
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Challenge questions Which institution is the most “important” provider for the Bronx?
- Hint: try a centrality measure Can you determine if geography plays a role in patient sharing in the Bronx?
- Which parameter could be used to partition the graph? Can you filter the graph to show only radiologists? Which radiologist has the highest “authority” in the graph?
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Other tools for graph analysis NetworkX
- Python - Lots of algorithms igraph
- R and Python Gremlin – graph traversal and manipulation
- Groovy shell - Gremlin interface is implemented for Neo4J And more . . .
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Scaling the analysis to the entire DocGraph Most healthcare graphs will be big (millions of nodes) What we learn at the local level can be applied at the global level
- Importance of geography - Supernodes (radiologist, ER docs, pathologist, transportation, …) Many graph measures don’t scale well
- Maximal cliques Currently exploring how to use Faunus to scale the analysis
with Hadoop
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Links http://strata.oreilly.com/2012/11/docgraph-open-social-doctor-data.html (information) https://github.com/jhajagos/DocGraph (code) http://notonlydev.com/docgraph-data/ (open source $1 covers bandwidth fees) https://groups.google.com/forum/#!forum/docgraph (mailing list)
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Questions
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Try to publish your own healthcare dataset as a graph!
