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Topic: Pharmacy Claims Fraud Detection Using Apache Spark
Speakers: Rajesh Inbasekaran & Giridharan Gurumoorthy
About Rajesh Inbasekaran
About Giridharan Gurumoorthy
With known fraud, waste and abuse data, supervised learning techniques like Random Forest, Neural Networks, etc., can be used to identify fraudulent transactions similar to historical fraud signatures. Combining outputs of these techniques Fraud scores are generated for each player [member, pharmacy and prescriber]. In this talk, we’re going to illustrate how machine learning [Spark MLLib and GraphX] was used to identify suspicious activity like co-conspiracies to commit fraud by pharmacies and prescribers[doctors] and others. We’re also going to demonstrate how fraud score was determined in this pharmacy claims fraud detection application.
Topic: Fiducial Marker Tracking Using Machine Vision
Speakers: Saurabh Ghanekar & Dr. Kazutaka Takahashi (University of Chicago)
About Saurabh Ghanekar
About Kazutaka Takahashi
Videos of rodents feeding on kibble are recorded by a high-speed biplanar videofluoroscopy technique (XROMM). Their feeding behavior is then analyzed by tracking radio-opaque fiducial markers implanted in their head region. The marker tracking process, until now, was manual and tedious, and was not designed to process massive amounts of longitudinal data. This session will highlight a near-automated, deep learning-based solution for detecting and tracking fiducial markers in the videos, resulting in a more efficient and robust process, with a 300+ times reduction in data processing time compared to a manual use of the existing software.
Our approach involved the following steps:(i) Marker Detection-Deep Learning algorithms were used to identify the pixels corresponding to markers within each frame; (ii) Marker Tracking-Kalman filtering along with Hungarian algorithm were used for tracking markers across frames; (iii) 2D to 3D Conversion- sequence matching of videos recorded by both cameras, and triangulating marker locations in 2D track coordinates to generate 3D marker locations. The features extracted from videos would be used to characterize behaviorally relevant kinematic features such as rhythmic chewing or swallowing. The solution involved the use of TensorFlow-Python APIs and Spark.
SAS Global Forum
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