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Tuesday, June 5 • 2:15pm - 3:00pm
Leveraging Machine Learning with 2.7 million Patients to Identify Top Risk Factors of Acute Respiratory Failure and Invasive Mechanical Ventilation

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Among the six most expensive principle procedures reported by U.S. Centers for Disease Control and Medtronic MITG HEOR team, Respiratory Intubation and Mechanical Ventilation were the top two procedures with highest aggregate cost and with the most costly growth trend line. This study aims to understand factors that contribute to patients with Acute Respiratory Failure progressing to Invasive Mechanical Ventilation. We will utilize both supervised and unsupervised Machine learning approaches to identify top risk factors as well as combined characteristics. Generalized estimating equation and generalized linear model will also be conducted to compare the machine learning results. SAS and R will be used for the analysis.

avatar for Hung-Lun Chien

Hung-Lun Chien

Director, Innovative Data Solutions, Medtronic

Tuesday June 5, 2018 2:15pm - 3:00pm
Garden Room (Kopp A-B-C) Normandale Partnership Center, 9700 France Ave So, Bloomington, MN 55431