Mattie Frantz Wasiak
MIT EECS | Quick Undergraduate Research and Innovation Scholar
Leveraging Clinical Data Sets to Optimize Oxygen Delivery to Newborns
- Artificial Intelligence and Machine Learning
Tight oxygen titration in the preterm neonate is a key aspect of neonatal intensive care due to the mortality associated with hypoxia (low oxygen saturation) and the morbidity associated with hyperoxia (high oxygen saturation) in this vulnerable population. Despite these known complications of sustained oxygenation outside target ranges, most Neonatal Intensive Care Units (NICUs) fail to reliably maintain infants’ oxygenation saturations within target range. The primary goal of this project is to leverage large volumes of physiological data streams collected in the NICU to identify clinical, demographic, physiological, and workflow factors that place preterm infants at risk for hypoxia and hyperoxia.
“I am participating in SuperUROP to gain more exposure to research that applies data analytics to health care. I have developed a passion for this field through previous interesting research experience, and I am excited to continue pursuing health care and learn how to contribute to it. I hope to learn a lot about the research process and improve my presentation skills in order to ultimately become more prepared for the Master’ s in Engineering (MEng) program.