Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT...
Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging
About this item
Full title
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Publisher
San Francisco: Public Library of Science
Journal title
Language
English
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Publication information
Publisher
San Francisco: Public Library of Science
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Scope and Contents
Contents
Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, alon...
Alternative Titles
Full title
Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_plos_journals_3069279691
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_3069279691
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0291451
