ArticleAIDS (London, England)2021
Utilizing electronic health record data to understand comorbidity burden among people living with HIV: a machine learning approach.
Article in AIDS (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- Article
- Healthcare Transition From Pediatric to Adult Care Among Youth Living With HIV in South Carolina: Retrospective Study From 2005 to 2021.The Journal of adolescent health : official publication of the Society for Adolescent Medicine · 2026Article
- Post-transition Care Retention and Viral Suppression Outcomes Among Youth Living with HIV in South Carolina: A Real-World Data Research.AIDS and behavior · 2026Article
- Disparities in cardiovascular disease prevalence among people living with HIV in South Carolina from 2005 to 2020.Public health in practice (Oxford, England) · 2025Article
- Leveraging Electronic Health Records and Claims Data to Improve HIV and Comorbidity Care Trajectories: A Scoping Review.Current HIV/AIDS reports · 2025Article
- Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study.Frontiers in cellular and infection microbiology · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Comorbidity Burden and Health Care Utilization by Substance use Disorder Patterns among People with HIV in Florida.AIDS and behavior · 2024Article
- Machine learning models based on fluid immunoproteins that predict non-AIDS adverse events in people with HIV.iScience · 2024Article
- The dynamic risk factors of cardiovascular disease among people living with HIV: a real-world data study.BMC public health · 2024Article
- Examining the Association Between a Modified Quan-Charlson Comorbidity Index and HIV Viral Suppression: A Cross-Sectional Analysis of DC Cohort Participants.AIDS research and human retroviruses · 2023Article
- Low incidence of advanced neurological burden but high incidence of age-related conditions that are dementia risk factors in aging people living with HIV: a data-linkage 10-year follow-up study.Journal of neurovirology · 2023Article
- Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals.International journal of general medicine · 2023Article
- Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.BMC infectious diseases · 2022Article
- Power of Big Data in ending HIV.AIDS (London, England) · 2021Article
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Authors and funding
6 authors.
Funding
Abstract
objectivesAn understanding of the predictors of comorbidity among people living with HIV (PLWH) is critical for effective HIV care management. In this study, we identified predictors of comorbidity burden among PLWH based on machine learning models with electronic health record (EHR) data.
methodsThe study population are individuals with a HIV diagnosis between January 2005 and December 2016 in South Carolina (SC). The change of comorbidity burden, represented by the Charlson Comorbidity Index (CCI) score, was measured by the score difference between pre- and post-HIV diagnosis, and dichotomized into a binary outcome variable. Thirty-five risk predictors from multiple domains were used to predict the increase in comorbidity burden based on the logistic least absolute shrinkage and selection operator (Lasso) regression analysis using 80% data for model development and 20% data for validation.
resultsOf 8253 PLWH, the mean value of the CCI score difference was 0.8 ± 1.9 (range from 0 to 21) with 2328 (28.2%) patients showing an increase in CCI score after HIV diagnosis. Top predictors for an increase in CCI score using the LASSO model included older age at HIV diagnosis, positive family history of chronic conditions, tobacco use, longer duration with retention in care, having PEBA insurance, having low recent CD4+ cell count and duration of viral suppression.
conclusionThe application of machine learning methods to EHR data could identify important predictors of increased comorbidity burden among PLWH with high accuracy. Results may enhance the understanding of comorbidities and provide the evidence based data for integrated HIV and comorbidity care management of PLWH.
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