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Exploring new possi...
Exploring new possibilities for case based explanation of artificial neural network ensembles
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- Green, Michael (author)
- Lund University,Lunds universitet,Beräkningsbiologi och biologisk fysik - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Computational Biology and Biological Physics - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science
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- Ekelund, Ulf (author)
- Lund University,Lunds universitet,Medicin, Lund,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Medicine, Lund,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine
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- Edenbrandt, Lars (author)
- Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Nuclear medicine, Malmö,Lund University Research Groups
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- Björk, Jonas (author)
- Lund University,Lunds universitet,Centrum för ekonomisk demografi,Ekonomihögskolan,Avdelningen för arbets- och miljömedicin,Institutionen för laboratoriemedicin,Medicinska fakulteten,Centre for Economic Demography,Lund University School of Economics and Management, LUSEM,Division of Occupational and Environmental Medicine, Lund University,Department of Laboratory Medicine,Faculty of Medicine
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- Lundager Hansen, Jakob (author)
- Skåne University Hospital
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- Ohlsson, Mattias (author)
- Lund University,Lunds universitet,Beräkningsbiologi och biologisk fysik - Genomgår omorganisation,Institutionen för astronomi och teoretisk fysik - Genomgår omorganisation,Naturvetenskapliga fakulteten,Computational Biology and Biological Physics - Undergoing reorganization,Department of Astronomy and Theoretical Physics - Undergoing reorganization,Faculty of Science
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(creator_code:org_t)
- Elsevier BV, 2009
- 2009
- English.
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In: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 22:1, s. 75-81
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Abstract
Subject headings
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- Artificial neural network (ANN) ensembles have long suffered from a lack of interpretability. This has severely limited the practical usability of ANNs in settings where an erroneous decision can be disastrous. Several attempts have been made to alleviate this problem. Many of them are based on decomposing the decision boundary of the ANN into a set of rules. We explore and compare a set of new methods for this explanation process on two artificial data sets (Monks 1 and 3), and one acute coronary syndrome data set consisting of 861 electrocardiograms (ECG) collected retrospectively at the emergency department at Lund University Hospital. The algorithms managed to extract good explanations in more than 84% of the cases. More to the point, the best method provided 99% and 91% good explanations in Monks data 1 and 3 respectively. Also there was a significant overlap between the algorithms. Furthermore, when explaining a given ECG, the overlap between this method and one of the physicians was the same as the one between the two physicians in this study. Still the physicians were significantly, p-value <0.001, more similar to each other than to any of the methods. The algorithms have the potential to be used as an explanatory aid when using ANN ensembles in clinical decision support systems.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Anestesi och intensivvård (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Anesthesiology and Intensive Care (hsv//eng)
Keyword
- Neural Network Ensembles
- Acute Coronary Syndrome
- Case-Based Explanation
- Sensitivity Analysis
Publication and Content Type
- art (subject category)
- ref (subject category)
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