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Comparison between ...
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Green, MichaelLund 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
(författare)
Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room
- Artikel/kapitelEngelska2006
Förlag, utgivningsår, omfång ...
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Elsevier BV,2006
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14 s.
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electronicrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:lup.lub.lu.se:fde44c88-5379-417c-a50f-794c47ed7a73
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https://lup.lub.lu.se/record/593190URI
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https://doi.org/10.1016/j.artmed.2006.07.006DOI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:art swepub-publicationtype
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Summary 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.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
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Björk, JonasLund University,Lunds universitet,Centrum för ekonomisk demografi,Ekonomihögskolan,Centre for Economic Demography,Lund University School of Economics and Management, LUSEM(Swepub:lu)ymed-jbj
(författare)
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Forberg, JakobSkåne University Hospital(Swepub:lu)ja8218lu
(författare)
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Ekelund, UlfSkåne University Hospital(Swepub:lu)mphy-uek
(författare)
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Edenbrandt, LarsSkåne University Hospital(Swepub:lu)klfy-led
(författare)
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Ohlsson, MattiasLund 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(Swepub:lu)thep-moh
(författare)
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Beräkningsbiologi och biologisk fysik - Genomgår omorganisationInstitutionen för astronomi och teoretisk fysik - Genomgår omorganisation
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Artificial Intelligence in Medicine: Elsevier BV38:3, s. 305-3181873-28600933-3657
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