Zernikow B, Holtmannspoetter K, Michel E, Pielemeier W, Hornschuh F, Westermann A, Hennecke K H
Vestische Kinderklinik Witten/Herdecke University, Datteln, Germany.
Arch Dis Child Fetal Neonatal Ed. 1998 Sep;79(2):F129-34. doi: 10.1136/fn.79.2.f129.
To predict the individual neonatal mortality risk of preterm infants using an artificial neural network "trained" on admission data.
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.
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.
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.
利用基于入院数据“训练”的人工神经网络预测早产儿个体的新生儿死亡风险。
我们的回顾性研究共纳入890例早产儿（胎龄<32周和/或出生体重<1500g）。神经网络基于1990年至1993年出生的婴儿进行训练。对随后三年出生的婴儿进行预测价值测试。
人工神经网络的表现显著优于逻辑回归模型（受试者工作特征曲线下面积分别为0.95和0.92）。如果预测死亡风险大于0.50，则生存与高发病率相关。没有预测死亡风险大于0.80的早产儿。两名出生体重>2000g且患有严重先天性疾病的非存活者的死亡风险在很大程度上被低估。
基于入院数据训练的人工神经网络能够准确预测大多数早产儿的死亡风险。然而，大量的预测失败使其不适用于个体治疗决策。
