ArticleBMC medical informatics and decision making2018
A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 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
- Developing a Naïve Bayes risk classification machine learning algorithm to predict high viral load in a low-resource setting.PLOS global public health · 2026Article
- Predicting HIV viral non-suppression in Uganda: development and validation of machine learning and risk stratification models using routine EMR data.Frontiers in artificial intelligence · 2026Article
- MRI-derived radiomics model for predicting intratumoral tertiary lymphoid structures in soft tissue sarcoma.Insights into imaging · 2025Article
- CT-Based Intrathrombus and Perithrombus Radiomics for Prediction of Prognosis after Endovascular Thrombectomy: A Retrospective Study across 2 Centers.AJNR. American journal of neuroradiology · 2025Article
- The Detection of COVID-19-Related Multivariate Biomarker Immune Response in Pediatric Patients: Statistical Aspects.Viruses · 2025Article
- Development and validation of a digital biomarker predicting acute kidney injury following cardiac surgery on an hourly basis.JTCVS open · 2023Article
- Dynamic Prediction of Patient Outcomes in the Intensive Care Unit: A Scoping Review of the State-of-the-Art.Journal of intensive care medicine · 2023Article
- Predicting Adolescent Intervention Non-responsiveness for Precision HIV Prevention Using Machine Learning.AIDS and behavior · 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
- Longitudinal patients' cohorts for impactful research and HIV care at the Infectious Diseases Institute.African health sciences · 2022Article
- SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data.GigaScience · 2022Article
- Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma.Journal of translational medicine · 2022Article
- Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system.BMC bioinformatics · 2022Article
- Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma.Computational and structural biotechnology journal · 2022Article
- Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa.PloS one · 2022Article
- Serum Interleukin-6 and Weight Loss in Antiretroviral-naïve and Antiretroviral-treated Patients with HIV/AIDS: Relationships and Predictors.Current HIV research · 2022Article
- Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.Current HIV/AIDS reports · 2021Review
Corrections and comments
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Authors and funding
5 authors.
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Abstract
backgroundTreatment with effective antiretroviral therapy (ART) lowers morbidity and mortality among HIV positive individuals. Effective highly active antiretroviral therapy (HAART) should lead to undetectable viral load within 6 months of initiation of therapy. Failure to achieve and maintain viral suppression may lead to development of resistance and increase the risk of viral transmission. In this paper three logistic regression based machine learning approaches are developed to predict early virological outcomes using easily measurable baseline demographic and clinical variables (age, body weight, sex, TB disease status, ART regimen, viral load, CD4 count). The predictive performance and generalizability of the approaches are compared.
methodsThe multitask temporal logistic regression (MTLR), patient specific survival prediction (PSSP) and simple logistic regression (SLR) models were developed and validated using the IDI research cohort data and predictive performance tested on an external dataset from the EFV cohort. The model calibration and discrimination plots, discriminatory measures (AUROC, F1) and overall predictive performance (brier score) were assessed.
resultsThe MTLR model outperformed the PSSP and SLR models in terms of goodness of fit (RMSE = 0.053, 0.1, and 0.14 respectively), discrimination (AUROC = 0.92, 0.75 and 0.53 respectively) and general predictive performance (Brier score= 0.08, 0.19, 0.11 respectively). The predictive importance of variables varied with time after initiation of ART. The final MTLR model accurately (accuracy = 92.9%) predicted outcomes in the external (EFV cohort) dataset with satisfactory discrimination (0.878) and a low (6.9%) false positive rate.
conclusionMultitask Logistic regression based models are capable of accurately predicting early virological suppression using readily available baseline demographic and clinical variables and could be used to derive a risk score for use in resource limited settings.
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