Predicting Responses to Mechanical Ventilation for Preterm Infants with Acute Respiratory Illness using Artificial Neural Networks

Brigham, Katharine; Gupta, Samir; Brigham, John C.

Abstract

Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real‐time, based on ventilation monitoring up to the point of analysis. In this work, recurrent neural networks are proposed for predicting future ventilation parameters due to the highly nonlinear behavior of the ventilation measures of interest and the ability of recurrent neural networks to model complex nonlinear functions. The resulting application of this particular class of neural networks shows promise in its ability to predict future responses for different ventilation modes. Towards improving care and treatment of preterm newborns, further development of this prediction process for ventilation could potentially aid in important clinical decisions or studies to improve preterm infant health.

Citation

Brigham, K., Gupta, S., & Brigham, J. C. (2018). Predicting Responses to Mechanical Ventilation for Preterm Infants with Acute Respiratory Illness using Artificial Neural Networks. International Journal for Numerical Methods in Biomedical Engineering, 34(8), Article e3094. https://doi.org/10.1002/cnm.3094

• Journal Article Type: Article
• Acceptance Date: Apr 4, 2018
• Online Publication Date: May 9, 2018
• Publication Date: Aug 1, 2018
• Deposit Date: Apr 4, 2018
• Publicly Available Date: May 9, 2019
• Journal: International Journal for Numerical Methods in Biomedical Engineering
• Print ISSN: 2040-7939
• Electronic ISSN: 2040-7947
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 34
• Issue: 8
• Article Number: e3094
• DOI: https://doi.org/10.1002/cnm.3094
• Public URL: https://durham-repository.worktribe.com/output/1335953