Association rule mining and network analysis of the evolving comorbidity patterns in HIV inpatients in Baise, China
Lihong Zhao, Liuying Tang, Xu Yang, Suren Rao Sooranna, Qiuping Li, Huiying Tan, Huina Guo

TL;DR
This study analyzes comorbidity patterns in HIV patients in Baise, China, using data from 2019 to 2024 to track changes and identify key diseases.
Contribution
The study introduces a novel temporal and network-based analysis of evolving comorbidity patterns in HIV patients using association rule mining and social network analysis.
Findings
99.48% of HIV inpatients had two or more comorbidities, with a median of 9 comorbidities per patient.
Comorbidity patterns evolved from (B20 + B37 → B99) in 2019 to (J18 + E87 → E46) by 2023.
Electrolyte imbalances (E87), HIV-related infections (B20), and candidiasis (B37) were core disease nodes in the network.
Abstract
With the widespread use of antiretroviral therapy, human immunodeficiency virus (HIV) infection is considered to be a manageable chronic disease, but it is accompanied by an increased burden of comorbidities. Baise is an area characterized by a high incidence of HIV infection in Guangxi, China. However, research on its comorbidity patterns is limited. This study aims to clarify the burden, patterns, network features, and temporal evolution of comorbidities among HIV inpatients in Baise. We collected electronic medical records from 3,294 HIV patients hospitalized at Baise People’s Hospital between January 2019 and August 2024. The Apriori algorithm was employed to extract association rules between diseases, while Gephi was utilized to construct comorbidity social network diagrams of the data. The findings revealed that 99.48% of patients presented with two or more comorbidities, with a…
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Taxonomy
TopicsHIV-related health complications and treatments · Chronic Disease Management Strategies · HIV/AIDS Research and Interventions
