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Prediction of mortality in an Indian intensive care unit - Comparison between APACHE II and artificial neural networks

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Title Prediction of mortality in an Indian intensive care unit - Comparison between APACHE II and artificial neural networks
 
Creator NIMGAONKAR, A
KARNAD, DR
SUDARSHAN, S
OHNO-MACHADO, L
KOHANE, I
 
Subject disease classification-system
logistic-regression models
international comparisons
hospital mortality
genetic algorithm
acute physiology
severity
probability
cohort
logistic regression
outcome prediction
prognostication
severity scoring
artificial intelligence
information gain
acute physiology and chronic health evaluation ii
goodness-of-fit
 
Description Objective. To compare hospital outcome prediction using an artificial neural network model, built on an Indian data set, with the APACHE II (Acute Physiology and Chronic Health Evaluation II) logistic regression model. Design. Analysis of a database containing prospectively collected data. Setting. Medical-neurological ICU of a university hospital in Mumbai, India. Subjects. Two thousand sixty-two consecutive admissions between 1996 and1998. Interventions. None. Measurements and results. The 22 variables used to obtain day-1 APACHE II score and risk of death were recorded. Data from 1,962 patients were used to train the neural network using a back-propagation algorithm. Data from the remaining 1,000 patients were used for testing this model and comparing it with APACHE II. There were 337 deaths in these 1,000 patients; APACHE II predicted 246 deaths while the neural network predicted 336 deaths. Calibration, assessed by the Hosmer-Lemeshow statistic, was better with the neural network (H=22.4) than with APACHE II (H=123.5) and so was discrimination (area under receiver operating characteristic curve =0.87 versus 0.77, p=0.002). Analysis of information gain due to each of the 22 variables revealed that the neural network could predict outcome using only 15 variables. A new model using these 15 variables predicted 335 deaths, had calibration (H=27.7) and discrimination (area under receiver operating characteristic curve =0.88) which was comparable to the 22-variable model (p=0.87) and superior to the APACHE II equation (p
 
Publisher SPRINGER-VERLAG
 
Date 2011-08-30T11:48:18Z
2011-12-26T12:58:55Z
2011-12-27T05:49:38Z
2011-08-30T11:48:18Z
2011-12-26T12:58:55Z
2011-12-27T05:49:38Z
2004
 
Type Article
 
Identifier INTENSIVE CARE MEDICINE, 30(2), 248-253
0342-4642
http://dx.doi.org/10.1007/s00134-003-2105-4
http://dspace.library.iitb.ac.in/xmlui/handle/10054/12301
http://hdl.handle.net/10054/12301
 
Language en