Development of a machine learning model for cardiovascular diseases risk prediction in older HIV/AIDS patients in Southern China from an eleven-year cohort study

Authors

Yue Zhao (ORCID: https://orcid.org/0000-0001-5116-1101)
Chuanyi Ning (ORCID: https://orcid.org/0000-0002-8748-2780)
Beibei Gong
Haimei Huang (ORCID: https://orcid.org/0009-0004-9726-077X)
Xuanlan Li
Xiaoyong Pan (ORCID: https://orcid.org/0000-0001-5010-464X)
Yuxuan Li (ORCID: https://orcid.org/0000-0002-5145-8607)
Jiahui Zhang (ORCID: https://orcid.org/0009-0003-8780-633X)
Chunyan Li (ORCID: https://orcid.org/0000-0001-9388-1799)
Sisi Li (ORCID: https://orcid.org/0000-0002-4862-8740)
Yunan Xu (ORCID: https://orcid.org/0000-0003-1158-6740)

Institutions

Guangxi University (CN)
Guangxi Medical University (CN)
First Affiliated Hospital of GuangXi Medical University (CN)
The Second Nanning People's Hospital (CN)

Publication Details

Journal: BMC Infectious Diseases
Published: 2025-12-09
DOI: https://doi.org/10.1186/s12879-025-12287-2
Primary Topic: HIV-related health complications and treatments
Type: article
Field-Weighted Citation Impact: 0.00

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article

Development of a machine learning model for cardiovascular diseases risk prediction in older HIV/AIDS patients in Southern China from an eleven-year cohort study

Yue Zhao, Chuanyi Ning, Beibei Gong, Haimei Huang et al.

Dec 9, 2025

BMC Infectious Diseases

HIV-related health complications and treatments

article

Development of a machine learning model for cardiovascular diseases risk prediction in older HIV/AIDS patients in Southern China from an eleven-year cohort study

Yue Zhao, Chuanyi Ning, Beibei Gong, Haimei Huang, Xuanlan Li, Xiaoyong Pan, Yuxuan Li, Jiahui Zhang, Chunyan Li, Sisi Li, Yunan Xu

December 9, 2025article en

Abstract

No abstract available for this paper.

BMC Infectious DiseasesVol. 26(1)

Guangxi University (CN), Guangxi Medical University (CN), First Affiliated Hospital of GuangXi Medical University (CN), The Second Nanning People's Hospital (CN)

Openalex Percentile: Top 45%

HIV-related health complications and treatments

DOI Open Access PDF OpenAlex

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