title: Predicting probability of mortality in the neonatal intensive care unit.
abstract: Artificial neural networks can be trained to predict outcomes in a neonatal intensive care unit (NICU). This paper expands on past research and shows that neural networks trained by the maximum likelihood estimation criterion will approximate the ;a posteriori probability' of NICU mortality. A gradient ascent method for the weight update of three-layer feed-forward neural networks was derived. The neural networks were trained on NICU data and the results were evaluated by performance measurement techniques, such as the Receiver Operating Characteristic Curve and the Hosmer-Lemeshow test. The resulting models applied as mortality prognostic screening tools are presented.
authors.affiliation: Program of Systems Science, University of Ottawa, Ontario, Canada. dzhou@site.uottawa.ca
authors.forename: Dajie
authors.initials: D
authors.lastname: Zhou
authors.forename: Monique
authors.initials: M
authors.lastname: Frize
mesh_terms: D002170:Canada / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D006801:Humans; D007226:Infant Mortality; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal / Q000706:statistics & numerical data*; 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; D016019:Survival Analysis; D015996:Survival Rate