title: Identifying risk factors for two complication types for neonatal intensive care patients (NICU).
abstract: This paper discusses the results of applying artificial neural networks to predicting complication for neonatal intensive care patients. Risk factors that lead to necrotizing entero-colitis or broncho-pulmonary dysplasia were identified. Future work will expand this work to other outcomes and add probability information to the estimations.
authors.affiliation: Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada. mfrize@connect.carleton.ca
authors.forename: Monique
authors.initials: M
authors.lastname: Frize
authors.forename: R C
authors.initials: RC
authors.lastname: Walker
authors.forename: Doaa
authors.initials: D
authors.lastname: Ibrahim
mesh_terms: D001997:Bronchopulmonary Dysplasia / Q000175:diagnosis / Q000453:epidemiology*; D002170:Canada / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D020345:Enterocolitis, Necrotizing / Q000175:diagnosis / Q000453:epidemiology*; D005103:Expert Systems; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal / Q000706:statistics & numerical data*; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015995:Prevalence; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity