Document Detail


Complimentary artificial neural network approaches for prediction of events in the neonatal intensive care unit.
MedLine Citation:
PMID:  19163742     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In the neonatal intensive care unit, the early and accurate prediction of mortality, length of stay and duration of ventilation can improve decision making. For physiological events, non-linear prediction models generally out-perform statistical-based approaches, as was confirmed in these experiments. For three medical outcomes, the maximum-likelihood (ML) approximation was used in conjunction with a gradient descent artificial neural network (ANN) prototype to create models with risk estimation ranges. The ML ANN showed that the ML estimation function was successful at creating variable sensitivity models for three important outcomes. The flexibility of the ML ANN in terms of output values differentiates it from the more traditional ANN.
Authors:
Daphne Townsend; Monique Frize
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference     Volume:  2008     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2008  
Date Detail:
Created Date:  2009-02-16     Completed Date:  2009-05-11     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  4605-8     Citation Subset:  IM    
Affiliation:
Dept. of Systems and Computer Engineering at Carleton University, USA. dtownsen@connect.carleton.ca
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Canada
Databases, Factual
Decision Support Techniques
Humans
Infant, Newborn
Intensive Care, Neonatal / organization & administration*,  standards*
Likelihood Functions
Models, Theoretical
Neural Networks (Computer)*
ROC Curve
Reproducibility of Results
Risk
Sensitivity and Specificity
Treatment Outcome

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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