The prevailing issue when working with Operating Room (OR) scheduling within a hospital setting is that it is difficult to schedule and predict available OR block times. This leads to empty and unused operating rooms leading to longer waiting times for patients for their procedures. Using multi-variate linear regression, Ayad Shammout and Denny Lee will show how they can predict available OR block times using Spark (SparkR, MLLib) resulting in better OR utilization and shorter wait times for patients.
Denny Lee is a hands-on data architect and developer / hacker with more than 15 years of experience developing internet-scale infrastructure, data platforms, and distributed systems for both On-Premises and Cloud. His key focuses surround solving complex large scale data problems – providing not only architectural direction but hands-on implementation of these systems. Experience in building greenfield teams as well as turn around / change catalyst. His current technical interests include Apache Spark, Big Data, Machine Learning, Graph databases, Cloud Infrastructure, and Distributed Systems Robustness.
Ayad Shammout is a Database & BI Specialist and Microsoft MVP. He has more than 20 years deep experience in Database technologies and specializing in SQL Server, SharePoint, and Windows OS. . Shammout is working in OLTP design and development, Data Warehousing, Business Intelligence and Big Data, with extensive experience in data management and analysis. Ayad has been involved in many SQL Server Enterprise implementations for High-Availability, Disaster Recovery, Infrastructure Design, Virtualization, Business Intelligence, Data Mining and Big Data.