Predicting length-of-stay in preterm neonates.

Abstract:

UNLABELLED:In neonatology, the early prediction of length-of-stay (LOS) may help in decision making. We retrospectively studied the accuracy of two LOS prediction models, namely a multiple linear regression model (MR) and an artificial neural network (ANN). Preterm neonates (n = 2144) were randomly assigned to a training-and-test (75%), or validation patient set (25%). A total of 40 first-day-of-life items (input data) and the date of discharge (output data) were routinely available. Training-and-test set data were used to identify input items with impact on LOS (input variables) using MR analysis to establish a MR prediction model and to train and test an ANN on those selected variables. Fed with validation set data, predicted LOS obtained from MR and ANN was compared individually with actual LOS. Predicted and actual LOS were highly correlated (for MR, r = 0.85 to 0.90; for ANN, r = 0.87 to 0.92). CONCLUSION:Even first-day-of-life data may contain substantial information with which to predict individual length-of-stay.

journal_name

Eur J Pediatr

authors

Zernikow B,Holtmannspötter K,Michel E,Hornschuh F,Groote K,Hennecke KH

doi

10.1007/s004310051010

subject

Has Abstract

pub_date

1999-01-01 00:00:00

pages

59-62

issue

1

eissn

0340-6199

issn

1432-1076

journal_volume

158

pub_type

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