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Severity of illness scoring in the i...

Doig, Gordon Stuart.

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Severity of illness scoring in the intensive care unit: A comparison of logistic regression and artificial neural networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Severity of illness scoring in the intensive care unit: A comparison of logistic regression and artificial neural networks./
Author:	Doig, Gordon Stuart.
Description:	141 p.
Notes:	Adviser: J. M. D. Robertson.
Contained By:	Dissertation Abstracts International60-09B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ42512
ISBN:	9780612425125

Severity of illness scoring in the intensive care unit: A comparison of logistic regression and artificial neural networks.
Doig, Gordon Stuart.

Severity of illness scoring in the intensive care unit: A comparison of logistic regression and artificial neural networks. - 141 p.

Adviser: J. M. D. Robertson.

Thesis (Ph.D.)--The University of Western Ontario (Canada), 1999.

Location. A 30 bed adult general intensive care unit (ICU) that serves a 600-bed tertiary care teaching hospital.

ISBN: 9780612425125Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Severity of illness scoring in the intensive care unit: A comparison of logistic regression and artificial neural networks.
LDR:03355nam 2200361 a 45 001 963252
005 20110831
008 110831s1999 ||||||||||||||||| ||eng d
020 $a 9780612425125
035 $a (UMI)AAINQ42512
035 $a AAINQ42512
040 $a UMI $c UMI
100 1 $a Doig, Gordon Stuart. $3 1286315
245 1 0 $a Severity of illness scoring in the intensive care unit: A comparison of logistic regression and artificial neural networks.
300 $a 141 p.
500 $a Adviser: J. M. D. Robertson.
500 $a Source: Dissertation Abstracts International, Volume: 60-09, Section: B, page: 4497.
502 $a Thesis (Ph.D.)--The University of Western Ontario (Canada), 1999.
520 $a Location. A 30 bed adult general intensive care unit (ICU) that serves a 600-bed tertiary care teaching hospital.
520 $a Patients. Consecutive patients with a duration of ICU stay greater than 72 hours. Outcome: ICU-based mortality.
520 $a Purpose. To compare the predictive performance of a series of logistic regression models (LMs) to a corresponding series of back-propagation artificial neural networks (ANNs).
520 $a The predictions obtained from ICU consultants (aROC 0.8210) discriminated significantly better than LMOT (aROC 0.6814, p = 0.0015) but there was no difference between the consultants and ANNOT (aROC 0.8094, p = 0.7684).
520 $a Methods. Data were collected on day one and day three of stay using a modified APACHE III methodology. A randomly generated 811 patient developmental database was used to build models using day one data (LM1 and ANN1), day three data (LM2 and ANN2) and a combination of day one and day three data (LMOT and ANNOT). Primary comparisons were based on area under the receiver operating curves (aROC) as measured on a 338 patient validation database. Outcome predictions were also obtained from experienced ICU clinicians on a subset of patients.
520 $a Results. Of the 3,728 patients admitted to the ICU during the period from March 1, 1994 through February 28, 1996, 1,181 qualified for entry into the study. There was no significant difference between LM and ANN models developed using day one data. The ANN developed using day three data performed significantly better than the corresponding LM (aROC LM2 0.7158 vs. ANN2 0.7845, p = 0.0355). The time dependent ANN model also performed significantly better than the corresponding LM (aROC LM OT 0.7342 vs. ANNOT 0.8095, p = 0.0140).
520 $a Conclusion. Although the 1,181 patients who became eligible for entry into this study represented only 32 percent of all ICU admissions, they accounted for 80 percent of the resources (costs) expended. ANNs demonstrated significantly better predictive performance in this clinically important group of patients. Four potential reasons are discussed: (1) ANNs are insensitive to problems associated with multicollinearity; (2) ANNs place importance on novel predictors; (3) ANNs automatically model nonlinear relationships and; (4) ANNs implicitly detect all possible interaction terms.
590 $a School code: 0784.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Health Sciences, Health Care Management. $3 1017922
650 4 $a Health Sciences, Medicine and Surgery. $3 1017756
690 $a 0308
690 $a 0564
690 $a 0769
710 2 $a The University of Western Ontario (Canada). $3 1017622
773 0 $t Dissertation Abstracts International $g 60-09B.
790 $a 0784
790 1 0 $a Robertson, J. M. D., $e advisor
791 $a Ph.D.
792 $a 1999
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ42512