Artificial neural network for risk assessment in preterm neonates

Abstract

Aim: To predict the individual neonatal mortality risk of preterm infants using an artificial neural network "trained" on admission data.

Methods: A total of 890 preterm neonates (< 32 weeks gestational age and/or < 1500 g birthweight) were enrolled in our retrospective study. The neural network trained on infants born between 1990 and 1993. The predictive value was tested on infants born in the successive three years.

Results: The artificial neural network performed significantly better than a logistic regression model (area under the receiver operator curve 0.95 vs 0.92). Survival was associated with high morbidity if the predicted mortality risk was greater than 0.50. There were no preterm infants with a predicted mortality risk of greater than 0.80. The mortality risks of two non-survivors with birthweights > 2000 g and severe congenital disease had largely been underestimated.

Conclusion: An artificial neural network trained on admission data can accurately predict the mortality risk for most preterm infants. However, the significant number of prediction failures renders it unsuitable for individual treatment decisions.