Craig G. Rusin, PhD, Assistant Professor of Pediatric Cardiology for Baylor College of Medicine / Texas Children's Hospital, spent a few more minutes after his presentation to talk a bit more with board member, Mark Anderson.

Patient surveillance is a critical factor in improving outcomes in critical care environments. This is most often done by increasing the number of nurses to patients for a particular unit. However, can we build a computer to provide a similar layer of surveillance?  Can we train an algorithm to recognize an acute event before it occurs, and how do we incorporate this automated surveillance into the clinical workflow? Join us to find out how Texas Children's Hospital is predicting arrest in their cardiac population using real-time predictive analytics. 

Learning Objectives

1. Introduction to bed side monitoring / patient surveillance
2. Introduction to machine learning
3. Example of predicting cardiac arrest in children
4. Incorporation of real-time predictive algorithm into clinical workflow
5. Data lake framework to advance multiple use cases for clinical analytics and research 

Craig G. Rusin, PhD
Assistant Professor - Pediatric Cardiology
Baylor College of Medicine / Texas Children's Hospital

Sponsored by Dell EMC - Advancing Healthcare within the digital era with best-in-class technology for Healthcare IT, Precision Medicine, Connected Health and Security.