Dr. Jeff Gilchrist is a Senior AI Data Scientist at the Children’s Hospital of Eastern Ontario (CHEO) Research Institute collaborating with physicians, researchers, and other teams on the use of machine learning. He completed his PhD in Systems and Computer Engineering and has been an Adjunct Research Professor at Carleton University since 2013.
Dr. Gilchrist’s research focus is predicting medical outcomes for hospital patients in the Neonatal Intensive Care Unit (NICU) using artificial intelligence and machine learning by analyzing data from patient vital signs and laboratory test results. Through this work, he has been collaborating with the NICU team at CHEO to design and develop clinical decision support systems and the necessary data collection system to use real-time evidence-based input to provide medical teams and parents with information to assist in the provision of care for babies.
Related News
Research Projects
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Predicting child and adolescent mental health emergency department revisits: a machine-learning approach compared to a clinician-derived baseline
01/12/2025
This study aimed to develop and validate a machine‑learning–based algorithm using electronic health record data to predict child and youth mental health emergency department revisits, and to compare its performance with a clinician‑weighted model. The machine learning approach outperformed the clinician-driven baseline while identifying clinically meaningful predictors such as prior ED visits, medication history, substance use, and outpatient mental health care. These findings demonstrate that interpretable ML models can complement clinical expertise and support improved planning for CYMH emergency care.
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Applying Data Preprocessing Methods to Predict Premature Birth
18/07/2018
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Developing an Automated Clinical Trending Tool for the Neonatal Intensive Care Unit (NICU)
30/05/2018