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 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.