Pediatric extracorporeal membrane oxygenation (ECMO) is used in emergent cases to provide life support for critically ill patients under the age of 17. However, there is a high mortality rate of pediatric ECMO patients (40%) which has mainly been attributed to the risk of acute neurologic injury. The purpose of this study is to analyze and predict neurologic injury through the development of a supervised machine learning model using data from the Pediatric ECMO Outcomes Registry (PEDECOR) database.

Team Members

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Project Mentors, Sponsors, and Partners

• Melania Bembea

• Dennis Leung

• Casey Overby Taylor

• Joseph L Greenstein

Course Faculty

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