Artificial neural network for risk assessment in preterm neonates

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 (

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Bibliographische Detailangaben
Veröffentlicht in:	Archives of disease in childhood. Fetal and neonatal edition 1998-09, Vol.79 (2), p.F129-F134
Hauptverfasser:	Zernikow, B, Holtmannspoetter, K, Michel, E, Pielemeier, W, Hornschuh, F, Westermann, A, Hennecke, K H
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Area Under Curve artificial neural network Biological and medical sciences Birth weight Body temperature Computerized, statistical medical data processing and models in biomedicine Female Health risks Humans Infant Mortality Infant, Newborn Infant, Premature Infant, Very Low Birth Weight Infants Intensive care Logistic Models Logistics Male Medical management aid. Diagnosis aid Medical sciences Morbidity Mortality Mortality risk Neonates Neural Networks (Computer) Original Physiology prediction Quality Regression analysis Retrospective Studies Risk Assessment Sensitivity and Specificity Software Variables
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Beschreibung
Zusammenfassung:	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 (
ISSN:	1359-2998 1468-2052
DOI:	10.1136/fn.79.2.F129