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Association rule mining and network analysis of the evolving comorbidity patterns in HIV inpatients in Baise, China

Lihong Zhao1, Liuying Tang2, Xu Yang1

  • 1Faculty of Nursing, Youjiang Medical University for Nationalities, Baise, China.

Frontiers in Public Health
|March 23, 2026
PubMed

Insights

HIV patients in Baise, China, experience a high burden of comorbidities, with most having multiple conditions. Key comorbidities like HIV infections and electrolyte imbalances form core networks, necessitating integrated care strategies.

Area of Science:

  • Infectious Diseases and Public Health
  • Medical Informatics
  • Network Medicine

Background:

  • Antiretroviral therapy has transformed human immunodeficiency virus (HIV) infection into a manageable chronic condition.
  • This transition has led to an increased prevalence and burden of comorbidities among HIV patients.
  • Limited research exists on comorbidity patterns in high-incidence areas like Baise, Guangxi, China.

Purpose of the Study:

  • To investigate the burden, patterns, network features, and temporal evolution of comorbidities in HIV inpatients in Baise.
  • To identify core disease nodes and their relationships within the comorbidity network.
  • To provide insights for clinical practice and patient management.

Main Methods:

  • Retrospective analysis of electronic medical records from 3,294 HIV patients (January 2019 - August 2024).
  • Application of the Apriori algorithm to determine disease association rules.
  • Utilized Gephi for constructing and analyzing comorbidity social network diagrams.

Main Results:

  • Nearly all (99.48%) patients had at least two comorbidities, with a median of 9 per patient.
  • The median number of comorbidities peaked at 11-12 during 2021-2022, decreasing to 7 in 2023-2024.
  • Comorbidity patterns evolved significantly over time, with HIV-related infections (B20), candidiasis (B37), and electrolyte imbalances (E87) identified as central network nodes.

Conclusions:

  • HIV patients in Baise face a substantial comorbidity burden, with evolving disease associations.
  • Specific comorbidity patterns can act as clinical early warning indicators.
  • Multidisciplinary care approaches are crucial for managing the complex health needs of HIV patients.

Keywords:
Apriori algorithmBaiseHIV inpatientscomorbidity patternsdynamic changesnetwork diagram

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