Predictions of coronary artery stenosis by artificial neural network
Data from angiography patient records comprised 14 input variables of a neural network. Outcomes (coronary artery stenosis or none) formed both supervisory and output variables. The network was trained by backpropagation on 332 records, optimized on 331 subsequent records, and tested on final 100 re...
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| Published in | Artificial intelligence in medicine Vol. 18; no. 3; pp. 187 - 203 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.03.2000
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Data from angiography patient records comprised 14 input variables of a neural network. Outcomes (coronary artery stenosis or none) formed both supervisory and output variables. The network was trained by backpropagation on 332 records, optimized on 331 subsequent records, and tested on final 100 records. If 0.40 was chosen as the output distinguishing stenosis from no stenosis, 81 patients who had stenosis would have been identified, while 9 of 19 patients who did not have stenosis might have been spared angiography. The results demonstrated that artificial neural networks could identify some patients who do not need coronary angiography. |
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| AbstractList | Data from angiography patient records comprised 14 input variables of a neural network. Outcomes (coronary artery stenosis or none) formed both supervisory and output variables. The network was trained by backpropagation on 332 records, optimized on 331 subsequent records, and tested on final 100 records. If 0.40 was chosen as the output distinguishing stenosis from no stenosis, 81 patients who had stenosis would have been identified, while 9 of 19 patients who did not have stenosis might have been spared angiography. The results demonstrated that artificial neural networks could identify some patients who do not need coronary angiography. |
| Author | Moore, William E. Schechter, Eliot Mobley, Bert A. Eichner, June E. McKee, Patrick A. |
| Author_xml | – sequence: 1 givenname: Bert A. surname: Mobley fullname: Mobley, Bert A. email: bert-mobley@ouhsc.edu organization: Department of Physiology, College of Medicine, University of Oklahoma, Oklahoma City, OK 73190, USA – sequence: 2 givenname: Eliot surname: Schechter fullname: Schechter, Eliot organization: Department of Medicine, College of Medicine, University of Oklahoma, Oklahoma City, OK 73190, USA – sequence: 3 givenname: William E. surname: Moore fullname: Moore, William E. organization: Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma, Oklahoma City, OK 73190, USA – sequence: 4 givenname: Patrick A. surname: McKee fullname: McKee, Patrick A. organization: Department of Medicine, College of Medicine, University of Oklahoma, Oklahoma City, OK 73190, USA – sequence: 5 givenname: June E. surname: Eichner fullname: Eichner, June E. organization: Department of Biostatistics and Epidemiology, College of Public Health, University of Oklahoma, Oklahoma City, OK 73190, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/10675714$$D View this record in MEDLINE/PubMed |
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| Keywords | Coronary angiography Outcome predictions Coronary artery disease Coronary artery stenosis Artificial neural networks |
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| SubjectTerms | Adult Aged Aged, 80 and over Artificial neural networks Coronary Angiography Coronary artery disease Coronary artery stenosis Coronary Disease - diagnosis Data evaluation Decision Making Female Humans Male Middle Aged Neural Networks (Computer) Outcome predictions Patient Selection Predictions |
| Title | Predictions of coronary artery stenosis by artificial neural network |
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