Adjunct Research Professor
|Degrees:||M.D. (Western Ontario)|
Dr. Erika Bariciak completed her medical training at the University of Western Ontario and her Pediatrics and Neonatal-Perinatal training at the University of Ottawa. She is an academic neonatologist at the Children’s Hospital of Eastern Ontario and the Ottawa Hospital General Campus. She collaborates with computer, biomedical, and electrical engineers at Carleton University and the University of Ottawa in the development of clinical decision support and outcome alert systems for the NICU. She also investigates novel neonatal abdominal imaging modalities, and conducts clinical studies investigating neonatal renal function and expanded newborn screening technologies.
- Fellow of the Royal College of Physicians and Surgeons of Canada, Pediatrics
- Fellow of the Royal College of Physicians and Surgeons of Canada, Neonatal-Perinatal Medicine
- GFT Academic Neonatologist, CHEO and Ottawa Hospital General Campus
- Associate Professor, University of Ottawa Faculty of Medicine, Department of Pediatrics
- Adjunct Research Professor, Carleton University, Department of Systems and Computer Engineering
- Clinician Investigator, Clinical Epidemiology Program, Ottawa Hospital Research Institute
- Clinician Investigator, Evidence to Practice Research Program, CHEO Research Institute
Top Scientific Accomplishments
- Co-developed a Physician and Parent Decision Support Tool: web based patient information hub for parents and physicians to support clinical decision making around changes in direction of care in the NICU; supported by machine learning mortality prediction models. Technology transfer phase.
- Co-developed a Preterm Birth Prediction Model using machine learning algorithms that analyzes maternal data acquired in the first 22 weeks of pregnancy and develops real-time alerts for patients predicted to be at increased risk for preterm delivery.
- Co-developed a Thermographic Imaging system that non-invasively records thermal maps of the abdomens of babies admitted to the NICU and uses machine learning-based analysis of the images to discern those images with necrotizing enterocolitis of the bowel from those that are normal.
- Co-developed an artificial intelligence-based mortality prediction model that used real-time patient data captured by a clinical data repository (also developed by our group) to predict mortality within 48h of admission with a better sensitivity and specificity than physician estimates alone.