Developing a nitroglycerin dose decision system & Machine Learning-based ocular pediatric innovation
Channel: T-CAIREM
Date: 05/10/2021

T-CAIREM Trainee Rounds
PRESENTERS: Navpreet Kamboj and Ryan Daniel
DATE: October 5, 2021 (Tuesday)
TIME: 12pm to 1pm
VENUE: Zoom
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Navpreet Kamboj
RN, MScN, PhD Student, Lawrence S. Bloomberg Faculty of Nursing

TALK TITLE: Developing a Nitroglycerin Dose Titration Decision Support System

Nurses in coronary care units use physiological monitoring devices to assist with the care of acutely ill cardiac patients. In particular, continuous or frequent monitoring of multiple physiological parameters is required to assist nurses’ decision-making about dose titration of vasoactive medications because of their narrow therapeutic spectrum and strong effect on hemodynamics. Nitroglycerin is a type of vasoactive medication and needs to be manually titrated. If the titrated dose is not optimal, patients can experience undesirable outcomes. Unfortunately, suboptimal titration of vasoactive medications is common. This research aims to develop a nitroglycerin dose titration decision support system that provides a forecast of future blood pressure measurements at the time of nitroglycerin dose titration. Nurses can use the system to augment their experience, education, and knowledge with information generated from the system to guide their decision-making.
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Ryan Daniel
Medical Student, Temerty Faculty of Medicine, University of Toronto

TALK TITLE: Machine Learning-Based Innovation in Ocular Pediatric Assessment Using Point of Care Ultrasound

Due to complex anatomic connections, pathological processes in the brain can lead to alterations in the structures of the eye. Ocular point of care ultrasound is a new method that is being used to detect these alterations. Here, we present preliminary work using machine learning in combination with ocular point of care ultrasound to detect signatures of increased intracranial pressure in pediatric patients.