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A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure

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Bibliographic Details
Published in:Journal of Biology and Life Science
Format: Online Article RSS Article
Published: 2026
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container_title Journal of Biology and Life Science
description
discipline_display Natural Sciences
discipline_facet Natural Sciences
format Online Article
RSS Article
genre Journal Article
id rss_article:19942
institution FRELIP
journal_source_facet Journal of Biology and Life Science
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure
Biology
Natural Sciences — Life Sciences
Natural Sciences
sub_discipline_display Natural Sciences — Life Sciences
sub_discipline_facet Natural Sciences — Life Sciences
subject_display Biology
Natural Sciences — Life Sciences
Natural Sciences
Biology
Natural Sciences — Life Sciences
Natural Sciences
subject_facet Biology
Natural Sciences — Life Sciences
Natural Sciences
title A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure
title_auth A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure
title_full A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure
title_fullStr A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure
title_full_unstemmed A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure
title_short A Machine Learning Approach for Predicting Failed First-Attempt Radial Artery Puncture Patients with Heart Failure
title_sort a machine learning approach for predicting failed first-attempt radial artery puncture patients with heart failure
topic Biology
Natural Sciences — Life Sciences
Natural Sciences
url https://www.macrothink.org/journal/index.php/jbls/article/view/23426