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Thursday, August 22 • 2:00pm - 2:45pm
Enterprise Studio Collections: Machine Learning at Scale

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The Disease Prediction and Progression OptumIQ Studio Collection contains over 200 models built to allow earlier detection of at-risk individuals, enabling providers to intervene more effectively, among many other use cases. These models span two lines of business, four modeling domains, and 25 distinct conditions. In order to build this Collection, we first needed to construct a generalized modeling framework. Leveraging the Customer Reporting Mart data and the cloud-computing resources made available via the OptumIQStudio Workbench, we developed a scalable modeling pipeline, reducing the time required to train hundreds of supervised, machine learning models to a matter of days. 

Speakers
avatar for Ahmed Kayal, MS

Ahmed Kayal, MS

Data Scientist, Optum
As an Optum Data Scientist, Ahmed's work centers around the development and implementation of machine learning models in the Healthcare space. With an interest in improving patient outcomes, he is largely focused on disease progression use cases.


Thursday August 22, 2019 2:00pm - 2:45pm
Room 211 595 Commonwealth Avenue, Boston, MA 02215, USA

Attendees (27)