A machine learning based approach to identify carotid subclinical atherosclerosis endotypes.

Authors:
Chen QS; Bergman O; Ziegler L; Baldassarre D; Veglia F and 10 more

Journal:
Cardiovasc Res

Publication Year: 2023

DOI:
10.1093/cvr/cvad106

PMCID:
PMC10730242

PMID:
37475157

View on PubMed View on PMC View Publisher
Journal Information

Full Title: Cardiovasc Res

Abbreviation: Cardiovasc Res

Country: Unknown

Publisher: Unknown

Language: N/A

Publication Details

Subject Category: Vascular Diseases

Available in Europe PMC: Yes

Available in PMC: Yes

PDF Available: No

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Evidence found in paper:

"the code used to generate endotypes is available at https://github com/qschenki/subclinical_athero_endotype/tree/main/ ."

Evidence found in paper:

"Conflict of interest: The authors disclose no conflicts of interest related to the present work."

Evidence found in paper:

"Funding The work was supported by Stiftelsen Sigurd & Elsa Goljes minne (LA2020-014) and Stiftelsen Professor Nanna Svartz fond (2020-00355 and 2021-00423), Gamla Tjänarinnor (2021-01114), Hjärtlungfonden (2021-0472), and ALF (2022-96005) to B.G. R.J.S. is supported by a University of Glasgow LKAS fellowship and a UKRI Innovation-HDR-UK fellowship (MR/S003061/1). The funding bodies had no role in the design of the study or in the interpretation of the results."

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Last Updated: Aug 05, 2025