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Wednesday, March 21 • 4:30pm - 5:00pm
Anomaly Detection in Medicare Provider Data using OAAgraph
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Graph analysis is an effective methodology in data analysis which considers fine-grained relationships between data entities. This session presents an example of anomaly detection using graph analysis on data stored in Oracle Database with Oracle R Enterprise and the OAAgraph package, using a real-world public data set.
Specifically, we analyze the public information of medical transactions from CMS (United States Center for Medicare and Medicaid Services) for the year 2012. We load this data set as a graph into Oracle Database, and use the OAAgraph package to apply graph analysis algorithms whose results are be visualized using R's ggplot2 visualization package. 
Our analysis successfully identifies suspicious records in the data set -- medical providers who perform procedures far from their specialties. We also discuss techniques how to reduce false positive outcomes. 

avatar for Sungpack Hong

Sungpack Hong

Research Director, Oracle Labs
Research Director at Oracle Labs.Leading projects regarding large-scale graph and data analysis -- platforms and applications
avatar for Mark Hornick

Mark Hornick

Director, Oracle
Mark Hornick is a Director for Oracle Advanced Analytics Product Management, focusing on Oracle R Enterprise (ORE), Oracle R Advanced Analytics for Hadoop (ORAAH), and the new Oracle Machine Learning for Python (OML4Py). He works with internal and external customers in the application... Read More →

Francisco Morales

Software Developer, Oracle Labs

Wednesday March 21, 2018 4:30pm - 5:00pm
2-Rm 102

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