Digital twin technology for HIV patient virtual modeling: a novel approach to treatment optimization

Background Human immunodeficiency virus (HIV) infection remains a global health challenge, necessitating personalized treatment strategies to optimize patient outcomes. Digital twin technology—synchronized with real-world entities—offers a pathway to precision medicine in HIV care. Methods We analyzed 5,436 HIV patients (2016–2024) with demographic, immunologic, and antiretroviral therapy (ART) information. Core algorithms comprised gradient boosting and random forest ensembles; deep architectures (LSTM and Transformer networks) were evaluated as comparative benchmarks on longitudinal feature tensors under identical supervision. Models used five-fold cross-validation, regularization, dropout, and early stopping. A probabilistic treatment ranking layer generated diversified regimen suggestions conditioned on model outputs. Results Ensemble models achieved strong explained variance on the registry-defined CD4 label (test R 2 up to 0.97; cross-validation stability reported in the Results). Deep models achieved comparable held-out accuracy on the same endpoint; the ranking module produced balanced regimen distributions in an illustrative optimization cohort. Simulated digital twin scenarios demonstrated counterfactual CD4 projections under alternative ART strategies for low, moderate, and higher baseline immunology profiles. Discussion The specific contribution is an explicit patient-state → prediction → regimen exploration pipeline suited to integration with telehealth analytics (risk dashboards, adherence supports), while transparently acknowledging endpoint and leakage limitations and the need for prospective outcome validation. A pragmatic HIV treatment optimization strategy is to route model-supported prioritization into clinician-facing dashboards and remote monitoring workflows rather than autonomous prescribing.