Predicting extubation outcome in preterm newborns: A comparison of neural networks with clinical expertise and statistical modeling

Pediatric Research

Volumn 56, Issue 1, 2004, Pages 11-18

  Mueller, Martina ^a   Wagner, Carol L ^b   Annibale, David J ^b   Hulsey, Thomas C ^b   Knapp, Rebecca G ^a   Almeida, Jonas S ^a  

^a MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

^b MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL VENTILATION; EXTUBATION; FEMALE; HUMAN; INTENSIVE CARE UNIT; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; NEONATAL RESPIRATORY DISTRESS SYNDROME; NEWBORN; NEWBORN CARE; NEWBORN INTENSIVE CARE; PREDICTION; PRIORITY JOURNAL; STATISTICAL MODEL; TIDAL VOLUME; TREATMENT OUTCOME;

DECISION MAKING; FEMALE; HUMANS; INFANT, NEWBORN; INFANT, PREMATURE; INTENSIVE CARE UNITS, NEONATAL; INTUBATION, INTRATRACHEAL; LOGISTIC MODELS; MALE; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; RETROSPECTIVE STUDIES; ROC CURVE; VENTILATOR WEANING;

EID: 3042601101     PISSN: 00313998     EISSN: None     Source Type: Journal    
DOI: 10.1203/01.PDR.0000129658.55746.3C     Document Type: Article

References (24)

1. Meadow W, Lantos J 2003 Ethics at the limit of viability: a premie's progress. Neoreviews 4:E157-E162
2. Spitzer AR 1996 Intensive Care of the Fetus and Neonate. Mosby, St. Louis, pp 553-570
3. Snowden S, Brownlee KG, Smye SW, Dear PRF 1993 An advisory system for artificial ventilation of the newborn utilizing a neural network. Med Inform 18:367-376
4. Zernikow B, Holtmannspoetter K, Michel E, Pielemeier W, Hornschuh F, Westermann A, Hennecke KH 1998 Artificial neural network for risk assessment in preterm neonates. Arch Dis Child Fetal Neonatal Ed 79:F129-F134
5. Ambalavanan N, Carlo WA 2001 Comparison of the prediction of extremely low birth weight neonatal mortality by regression analysis and by neural networks. Early Hum Dev 65:123-137
6. Braithwaite EA, Dripps J, Lyon AJ, Murray A 2001 Neural networks for neonatal intensive care. In: Dybowski R, Grant V (eds) Clinical Applications of Artificial Neural Networks. Cambridge University Press, Cambridge, pp 102-119
7. Schumacher M, Rossner R, Vach W 1996 Neural networks and logistic regression: part I. Comput Stat Data Anal 21:661-682
8. Landry MD, Sibbald WJ 2001 From data to evidence: evaluative methods in evidence-based medicine. Respir Care 46:1226-1235
9. Weigend AS, Rumelhart DE, Huberman BA 1991 Generalization by weight elimination with application to forecasting. In: Lippmann RP, Moody J, Touretzky DS (eds) Advances in Neural Information Processing Systems 3. Morgan Kaufman, San Francisco, pp 875-882
10. Bishop CM 1995 Neural Networks for Pattern Recognition. Clarendon Press, Oxford, pp 116-377
11. Reed RD, Marks RJ 2nd 1999 Neural Smithing - Supervised Learning in Feedforward Artificial Neural Networks. A Bradford Book, MIT Press, Cambridge, pp 239-290
12. Almeida J 2002 Predictive non-linear modeling of complex data by artificial neural networks. Curr Opin Biotechnol 13:72-76
13. Myers RH 1990 Classical and Modern Regression with Applications. 2nd Ed. Duxbury Press, Belmont, pp 317-325
14. Goodenough DJ, Rossman K, Lusted LB 1974 Radiographic applications of receiver operating characteristic (ROC) curves. Radiology 110:89-95
15. York J, DeVoe M 2002 Health issues in survivors of prematurity. South Med J 95:969-976
16. Ennett CM, Frize M, Walker CR 2001 Influence of missing values on artificial neural network performance. Medinfo 10:449-453
17. Carlo WA, Martin RJ 1986 Principles of neonatal assisted ventilation. Pediatr Clin North Am 33:221-37
18. SAS Software, Version 8.2 of the SAS System for Windows 95/98. SAS Institute Inc., Cary, NC
19. Bradley AP 1997 The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pat Rec 30:1145-1159
20. Cantor SB, Kattan MW 2000 Determining the area under the ROC curve for a binary diagnostic test. Med Decis Making 20:468-470
21. Sargent DJ 2001 Comparison of artificial neural networks with other statistical approaches-results from medical data sets. Cancer 91:1636-1642
22. Clermont G, Angus DC, DiRusso SM, Griffin M, Linde-Zwirble WT 2001 Predicting hospital mortality for patients in the intensive care unit: a comparison of artificial neural networks with logistic regression models. Crit Care Med 29:291-296
23. Nguyen T, Malley R, Inkelis S, Kuppermann N 2002 Comparison of prediction models of adverse outcome in pediatric disease using artificial neural networks and logistic regression analyses. J Clin Epidemiol 55:687-695
24. Lisboa PJ 2002 A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 15:11-39