Clinical Chemistry
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Clinical Chemistry 43: 1919-1925, 1997;
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (19)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Ellenius, J.
Right arrow Articles by Wallentin, L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ellenius, J.
Right arrow Articles by Wallentin, L.
Related Collections
Right arrow Laboratory Management
Right arrow Evidence Based Laboratory Medicine and Test Utilization
(Clinical Chemistry. 1997;43:1919-1925.)
© 1997 American Association for Clinical Chemistry, Inc.

Articles

Early assessment of patients with suspected acute myocardial infarction by biochemical monitoring and neural network analysis

Johan Elleniusa, Torgny Groth, Bertil Lindahl1 and Lars Wallentin1

Department of Biomedical Informatics and Systems Analysis, and
1 Department of Cardiology, University of Uppsala, Uppsala, Sweden.
2 The term "validation set" is used to denote a set of example cases used to tune the parameters of a classifier.
a Address correspondence to this author at: Department of Biomedical Informatics and Systems Analysis, University hospital, S-751 85 Uppsala, Sweden. Fax +46 18–531202; e-mail Johan.Ellenius{at}BMSA.uu.se

Neural network analysis was applied for early diagnosis/exclusion of acute myocardial infarction (AMI), prediction of infarct size, and estimation of "time from onset of infarction." Eighty-eight patients admitted within 8 h after onset of chest pain were included. Blood samples for measurement of myoglobin, creatine kinase isoform MB, and troponin T were obtained every 30 min during the first 3 h and then after successively longer intervals. Data from 50 patients were used to train a set of neural network components of a decision support system. The performance of the system was evaluated and compared with experienced clinicians for the remaining 38 patients. The computer system detected myocardial infarction and predicted infarct size earlier than the clinicians, but did not differ significantly in terms of diagnostic sensitivity, specificity, and predictive values when disregarding time for diagnosis. With a cross-validation procedure the cumulated sensitivities of the computer system for the first five measurements were estimated to be (mean ± 2SEM, n = 100): 0.77 ± 0.03, 0.89 ± 0.02, 0.94 ± 0.02, 0.97 ± 0.01, and 0.99 ± 0.01, respectively, with corresponding cumulated specificities between 0.93 ± 0.01 and 0.91 ± 0.01. We concluded that neural network analysis of serial measurements of biochemical markers might provide useful support for the early assessment of patients with suspected AMI.




The following articles in journals at HighWire Press have cited this article:


Home page
 Clin. Chem.Home page
J. R. Lakowicz, J. Malicka, E. Matveeva, I. Gryczynski, and Z. Gryczynski
Plasmonic Technology: Novel Approach to Ultrasensitive Immunoassays
Clin. Chem., October 1, 2005; 51(10): 1914 - 1922.
[Abstract] [Full Text] [PDF]


HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Copyright © 1997 by the American Association for Clinical Chemistry.