Complimentary artificial neural network approaches for prediction of events in the neonatal intensive care unit.
In the neonatal intensive care unit, the early and accurate prediction of mortality, length of stay and duration of ventilation can improve decision making. For physiological events, non-linear prediction models generally out-perform statistical-based approaches, as was confirmed in these experiments. For three medical outcomes, the maximum-likelihood (ML) approximation was used in conjunction with a gradient descent artificial neural network (ANN) prototype to create models with risk estimation ranges. The ML ANN showed that the ML estimation function was successful at creating variable sensitivity models for three important outcomes. The flexibility of the ML ANN in terms of output values differentiates it from the more traditional ANN.
Townsend
Daphne
D
Dept. of Systems and Computer Engineering at Carleton University, USA. dtownsen@connect.carleton.ca
Frize
Monique
M
D000465:Algorithms; D002170:Canada; D016208:Databases, Factual; D003661:Decision Support Techniques; D006801:Humans; D007231:Infant, Newborn; D015931:Intensive Care, Neonatal / Q000458:organization & administration* / Q000592:standards*; D016013:Likelihood Functions; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012372:ROC Curve; D015203:Reproducibility of Results; D012306:Risk; D012680:Sensitivity and Specificity; D016896:Treatment Outcome