Biomarker Identification for the Comorbidities of Acute Coronary Syndrome based on LassoNet Feature Selection-Reference-Cited by-同舟云学术

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Biomarker Identification for the Comorbidities of Acute Coronary Syndrome based on LassoNet Feature Selection

Published:2023-10-20 Issue: Volume:21 Page:1106-1110
ISSN:
Container-title:Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science
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Author:

Xu Xiaolu¹^ORCID,Wang Yuxing²^ORCID,Qi Zitong³^ORCID,Chen Junfeng⁴^ORCID,Geng Zhaohong²^ORCID,Hou Lina⁵^ORCID

Affiliation:

1. School of Computer and Artificial Intelligence, Liaoning Normal University, China

2. Department of Cardiology, Second Affiliated Hospital of Dalian Medical University, China

3. Department of Statistics, University of Washington, USA

4. School of Public Health, Dalian Medical University, China

5. Faculty of Education, Liaoning Normal University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3644116.3644305

Reference11 articles.

1. Clinical Features of Precocious Acute Coronary Syndrome

2. Effects of alirocumab on cardiovascular and metabolic outcomes after acute coronary syndrome in patients with or without diabetes: a prespecified analysis of the ODYSSEY OUTCOMES randomised controlled trial

3. Ren Hao "Predicting acute onset of heart failure complicating acute coronary syndrome: an explainable machine learning approach." Current Problems in Cardiology 48.2. 2023: 101480.

4. K. Efstratios M. Dimitrios V P. Andreas S P. Eleftherios D. Olga S. Nikolaos and 2022. Prognostic significance of metabolomic biomarkers in patients with diabetes mellitus and coronary artery disease. Cardiovascular Diabetology 21(1) 1-10.

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