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Decision Support fo...
Decision Support for the Initial Triage of Patients with Acute Myocardial Infarction
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- Olsson, Sven-Erik (författare)
- Lund University,Lunds universitet,Kliniska Vetenskaper, Helsingborg,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Clinical Sciences, Helsingborg,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine
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- Ohlsson, Mattias (författare)
- 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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- Öhlin, Hans (författare)
- Lund University,Lunds universitet,Kardiologi,Sektion II,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Cardiology,Section II,Department of Clinical Sciences, Lund,Faculty of Medicine
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Dzaferagic, Samir (författare)
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Nilsson, Marie-Louise (författare)
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Sandkull, Per (författare)
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- Edenbrandt, Lars (författare)
- Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Nuclear medicine, Malmö,Lund University Research Groups
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(creator_code:org_t)
- 2006
- 2006
- Engelska.
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Ingår i: Clinical Physiology and Functional Imaging. - 1475-0961. ; 26:3, s. 151-156
- Relaterad länk:
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http://www.ncbi.nlm....
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Objectives: To develop an automated tool for the analysis of electrocardiograms (ECG) with respect to changes that make the patient a candidate for reperfusion therapy. An additional aim was to assess the influence of the tool on the ECG classifications of three interns. Methods and Results: An artificial neural network was trained to interpret ECGs regarding changes making the patient a candidate for reperfusion therapy. The ECG measurements used as input to the network were obtained from the measurement program of the ECG recorders. The network was trained using a database of 3000 ECGs recorded at an emergency department. In the second step three interns classified 1000 test ECGs twice at different occasions, first without and thereafter with the advice of the neural network. The gold standard of the training and test ECGs was the classification of two experienced cardiologists. The three interns showed on average a sensitivity of 68% at a specificity of 92% without the advice of the neural network and a sensitivity of 93% at a specificity of 87% with the advice. The neural network itself showed a sensitivity of 95% at a specificity of 88%. The increase in sensitivity of 23-26% was highly significant (p<0.001) for all three interns. Conclusion: Artificial neural networks can be trained to gain a performance in the interpretation of ST-segment changes in accordance to experienced cardiologists. The neural networks offers a reliably support to physicians with lesser experience in the interpretation of ECG with respect to changes that make the patient a candidate for reperfusion therapy.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Fysiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Physiology (hsv//eng)
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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