Document Detail


Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room.
MedLine Citation:
PMID:  16962295     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
OBJECTIVE: Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. METHODS AND MATERIALS: Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients presenting an emergency department with chest pain. Only data immediately available at patient presentation were used, including electrocardiogram (ECG) data. The models were analyzed using receiver operating characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared with the odds ratios obtained from the logistic model. RESULTS: The ANN ensemble approach together with ECG data preprocessed using principal component analysis resulted in an area under the ROC curve of 80%. At the sensitivity of 95% the specificity was 41%, corresponding to a negative predictive value of 97%, given the ACS prevalence of 21%. Adding clinical data available at presentation did not improve the ANN ensemble performance. Using the area under the ROC curve and model calibration as measures of performance we found an advantage using the ANN ensemble models compared to the logistic regression models. CONCLUSION: Clinically, a prediction model of the present type, combined with the judgment of trained emergency department personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.
Authors:
Michael Green; Jonas Björk; Jakob Forberg; Ulf Ekelund; Lars Edenbrandt; Mattias Ohlsson
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Publication Detail:
Type:  Comparative Study; Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't     Date:  2006-09-07
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  38     ISSN:  0933-3657     ISO Abbreviation:  Artif Intell Med     Publication Date:  2006 Nov 
Date Detail:
Created Date:  2006-11-20     Completed Date:  2007-07-03     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  305-18     Citation Subset:  IM    
Affiliation:
Department of Theoretical Physics, Lund University, Sölvegatan 14A, SE-22362 Lund, Sweden. michael@thep.lu.se
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MeSH Terms
Descriptor/Qualifier:
Acute Disease
Aged
Calibration
Coronary Disease / diagnosis*
Emergency Service, Hospital*
Female
Humans
Logistic Models
Male
Middle Aged
Neural Networks (Computer)*
Risk Factors

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


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