Optimal Cut-off Thresholds of LGE Extent to Predict All-cause Death Using Machine-learning in a Larg...
Optimal Cut-off Thresholds of LGE Extent to Predict All-cause Death Using Machine-learning in a Large Cohort of ICM Patients Optimal Cut-off Thresholds of LGE Extent to Predict All-cause Death Using Machine-learning in a Large Cohort of ICM Patients
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Author / Creator
Alexandre Unger, MSc , Solenn Toupin, PhD , Theo Pezel, MD , Thierry Unterseeh, MD , Thomas Hovasse, MD , Stéphane Champagne, MD, PhD , Tania Ah-Sing, MD , Lounis Hamzi, MD , Trecy Gonçalves, MD , Jean Guillaume Dillinger, MD, PhD , Patrick Henry, MD, PhD , Valérie Bousson, MD, PhD , Francesca Sanguineti, MD , Philippe Garot, MD and Jérome Garot, MD, PhD
Publisher
Elsevier
Journal title
Language
English
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Publisher
Elsevier
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Full title
Optimal Cut-off Thresholds of LGE Extent to Predict All-cause Death Using Machine-learning in a Large Cohort of ICM Patients Optimal Cut-off Thresholds of LGE Extent to Predict All-cause Death Using Machine-learning in a Large Cohort of ICM Patients
Authors, Artists and Contributors
Author / Creator
Solenn Toupin, PhD
Theo Pezel, MD
Thierry Unterseeh, MD
Thomas Hovasse, MD
Stéphane Champagne, MD, PhD
Tania Ah-Sing, MD
Lounis Hamzi, MD
Trecy Gonçalves, MD
Jean Guillaume Dillinger, MD, PhD
Patrick Henry, MD, PhD
Valérie Bousson, MD, PhD
Francesca Sanguineti, MD
Philippe Garot, MD
Jérome Garot, MD, PhD
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TN_cdi_doaj_primary_oai_doaj_org_article_d5d534d4b12d4e39ae02add74eb78496
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d5d534d4b12d4e39ae02add74eb78496
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ISSN
1097-6647
