| An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: evaluation and comparison with serum myoglobin measurements. | |
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MedLine Citation:
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PMID: 9034674 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Recent studies have confirmed that artificial neural networks (ANNs) are adept at recognising patterns in sets of clinical data. The diagnosis of acute myocardial infarction (AMI) in patients presenting with chest pain remains one of the greatest challenges in emergency medicine. The aim of this study was to evaluate the performance of an ANN trained to analyse clinical data from chest pain patients. The ANN was compared with serum myoglobin measurements--cardiac damage is associated with increased circulating myoglobin levels, and this is widely used as an early marker for evolving AMI. We used 39 items of clinical and ECG data from the time of presentation to derive 53 binary inputs to a back propagation network. On test data (200 cases), overall accuracy, sensitivity, specificity and positive predictive value (PPV) of the ANN were 91.8, 91.2, 90.2 and 84.9% respectively. Corresponding figures using linear discriminant analysis were 81.0, 77.9, 82.6 and 69.7% (P < 0.01). Using a further test set from a different centre (91 cases), the accuracy, sensitivity, specificity and PPV for the admitting physicians were 65.1, 28.5, 76.9 and 28.6% respectively compared with 73.6, 52.4, 80.0 and 44.0% for the ANN. Although myoglobin at presentation was highly specific, it was only 38.0% sensitive, compared with 85.7% at 3 h. Simple strategies to combine clinical opinion, ANN output and myoglobin at presentation could greatly improve sensitivity and specificity of AMI diagnosis. The ideal support for emergency room physicians may come from a combination of computer-aided analysis of clinical factors and biochemical markers such as myoglobin. This study demonstrates that the two approaches could be usefully combined, the major benefit of the decision support system being in the first 3 h before biochemical markers have become abnormal. |
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Authors:
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R L Kennedy; R F Harrison; A M Burton; H S Fraser; W G Hamer; D MacArthur; R McAllum; D J Steedman |
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Publication Detail:
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Type: Comparative Study; Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Computer methods and programs in biomedicine Volume: 52 ISSN: 0169-2607 ISO Abbreviation: Comput Methods Programs Biomed Publication Date: 1997 Feb |
Date Detail:
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Created Date: 1997-05-19 Completed Date: 1997-05-19 Revised Date: 2007-11-15 |
Medline Journal Info:
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Nlm Unique ID: 8506513 Medline TA: Comput Methods Programs Biomed Country: IRELAND |
Other Details:
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Languages: eng Pagination: 93-103 Citation Subset: IM |
Affiliation:
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City Hospitals Sunderland, Department of Medicine, UK. |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Adult Aged Aged, 80 and over Angina Pectoris / diagnosis Chest Pain / diagnosis Decision Support Techniques Diagnosis, Computer-Assisted* / statistics & numerical data Emergency Service, Hospital Evaluation Studies as Topic Female Humans Male Middle Aged Myocardial Infarction / blood*, diagnosis* Myoglobin / blood* Neural Networks (Computer)* Prospective Studies Sensitivity and Specificity |
| Chemical | |
Reg. No./Substance:
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0/Myoglobin |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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