Using Advanced Machine Learning Models to Predict Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula

We collected demographics, validated vital signs, respiratory support settings, medications, and medical history on 433 patients under 24 months of age placed on HFNC in the Johns Hopkins Children’s Center Pediatric Intensive Care Unit from January 2019 through October 2020. Tree-based machine learning algorithms were trained to predict flow rate escalation at lead times varying from 1 to 12 hours. A long short-term memory (LSTM) neural network was trained to forecast future HFNC flow rates based on multivariate sequence data collected from a patient’s electronic health record.