Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations
Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations
About this item
Full title
Author / Creator
Doudesis, Dimitrios , Lee, Kuan Ken , Boeddinghaus, Jasper , Bularga, Anda , Ferry, Amy V. , Tuck, Chris , Lowry, Matthew T. H. , Lopez-Ayala, Pedro , Nestelberger, Thomas , Koechlin, Luca , Bernabeu, Miguel O. , Neubeck, Lis , Anand, Atul , Schulz, Karen , Apple, Fred S. , Parsonage, William , Greenslade, Jaimi H. , Cullen, Louise , Pickering, John W. , Than, Martin P. , Gray, Alasdair , Mueller, Christian , Mills, Nicholas L. and CoDE-ACS Investigators
Publisher
New York: Nature Publishing Group US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Nature Publishing Group US
Subjects
More information
Scope and Contents
Contents
Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with...
Alternative Titles
Full title
Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations
Authors, Artists and Contributors
Author / Creator
Lee, Kuan Ken
Boeddinghaus, Jasper
Bularga, Anda
Ferry, Amy V.
Tuck, Chris
Lowry, Matthew T. H.
Lopez-Ayala, Pedro
Nestelberger, Thomas
Koechlin, Luca
Bernabeu, Miguel O.
Neubeck, Lis
Anand, Atul
Schulz, Karen
Apple, Fred S.
Parsonage, William
Greenslade, Jaimi H.
Cullen, Louise
Pickering, John W.
Than, Martin P.
Gray, Alasdair
Mueller, Christian
Mills, Nicholas L.
CoDE-ACS Investigators
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10202804
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10202804
Other Identifiers
ISSN
1078-8956,1546-170X
E-ISSN
1546-170X
DOI
10.1038/s41591-023-02325-4
