Artificial neural network for risk assessment in preterm neonates
a Vestische
Kinderklinik Witten/Herdecke University Lloydstr. 5 D-45711 Datteln Germany, b Medizinischer Dienst
der Krankenversicherung Westfalen-Lippe, Muenster,
Germany
Correspondence to: Dr Boris Zernikow.Email:Boris.Zernikow{at}t-online.de
Accepted 9 March 1998
AIM
To predict the individual neonatal mortality
risk of preterm infants using an artificial neural network
"trained" on admission data.
METHODSA 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.
RESULTSThe 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.
CONCLUSIONAn 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.
© 1998 by Archives of Disease in Childhood
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