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Peer-Reviewed Publication
Healthc Technol Lett2024;11(4):252-257.August 1, 2024Journal Article

Machine learning modelling for predicting the utilization of invasive and non-invasive ventilation throughout the ICU duration.

Emma Schwager1, Mohsen Nabian2, Xinggang Liu3, Ting Feng1, Robin French4, Pam Amelung4, Louis Atallah4, Omar Badawi5
1Philips Research North America Cambridge Massachusetts USA.
2Philips Clinical AI and Analytics New Brunswick New Jersey USA.
3Johnson and Johnson Limited New Brunswick New Jersey USA.
4Philips EMR & Care Management Cambridge Massachusetts USA.
5Trial Library San Francisco California USA.
PubMed PMC DOI PDF

Abstract

The goal of this work is to develop a Machine Learning model to predict the need for both invasive and non-invasive mechanical ventilation in intensive care unit (ICU) patients. Using the Philips eICU Research Institute (ERI) database, 2.6 million ICU patient data from 2010 to 2019 were analyzed. This data was randomly split into training (63%), validation (27%), and test (10%) sets. Additionally,…

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