Jun 2026· Informatics· Vol 13, pp. 103· 0 citations· 54 references
TL;DR
The proposed framework provides a structured approach to support HIV risk stratification and personalized PrEP recommendations in a clinician-aligned manner and is preliminarily evaluated for implementation, generalizability, and impact on service engagement and PrEP initiation.
Abstract
Background: Although pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, identifying individuals who may benefit from PrEP and delivering personalized prevention recommendations remain challenging in routine and digital health settings. Objective: This study aimed to develop and preliminarily evaluate an integrated artificial intelligence framework combining machine learning (ML) for HIV risk stratification and generative artificial intelligence (GenAI) for personalized PrEP recommendation support. Methods: A curated dataset of 2000 de-identified client profiles from Love2Test platform was used for proof-of-concept model development. Profiles were labeled as low or high HIV acquisition risk by domain experts based on structured behavioral information. Multiple ML classifiers were trained and compared using PyCaret. The selected model was integrated with a generative AI model through structured prompting to generate personalized PrEP recommendation content. The integrated framework was evaluated through structured physician assessment by four independent medical doctors. Results: The selected model showed strong internal discrimination for classifying high versus low HIV acquisition risk. The integrated framework also received favorable physician evaluation for clinical accuracy, explanation validity, contextual relevance, and error minimization across fixed and randomly selected profiles. However, because expert labeling was based on structured behavioral indicators closely related to the model inputs, the high internal performance should be interpreted within the context of this proof-of-concept study. Conclusions: The proposed framework provides a structured approach to support HIV risk stratification and personalized PrEP recommendations in a clinician-aligned manner. However, this study was an offline proof-of-concept and did not directly evaluate patient interaction, PrEP uptake, stigma, adherence, or clinical outcomes. Prospective studies using larger and more representative real-world datasets are needed to assess implementation, generalizability, and impact on service engagement and PrEP initiation.
An AI-based platform that combines multiple screening modalities with AI-driven digital analysis for identification of high-risk individuals to improve cancer screening, support clinical decision-making, reduce cancer risk, and optimize healthcare resources is developed.
Aida Yavari Kondori, Ahmadreza Tavasouli, Mona Maftouh et al.· Clinical Cancer Research· 0 citations
BACKGROUND
Machine learning (ML), deep learning (DL) and other predictive modelling approaches are increasingly applied to predict antiretroviral therapy (ART) outcomes among people living with HIV, yet their methodological robustness and suitability for clinical and digital health integration remain uncertain.
OBJECTIVE
To systematically review artificial intelligence (AI)-based models predicting ART outcomes among people living with HIV, focusing on predictors, methodological rigour, validation practices and clinical integration readiness.
METHODS
For this review, AI was considered an umbrella concept encompassing ML and deep learning methods, while conventional predictive models such as logistic regression were synthesized separately were included in comparative studies. This review followed PRISMA 2020 guidelines and was registered in PROSPERO (CRD420251166548). Seven databases were searched from January 2000 to 25 September 2025. Data were extracted on model types, predictors, outcomes, validation strategies and performance metrics. Methodological quality and reporting completeness were assessed using PROBAST and the TRIPOD Statement. We proposed an exploratory clinical informatics framework for evaluating the translational readiness across data, workflow, interoperability and operational domains. This framework was applied to assess characteristics relevant to clinical readiness.
RESULTS
Twenty-six studies were included. Virologic outcomes (34.6%), retention in care (30.8%) and adherence (19.2%) were most common. Ensemble methods, particularly Random Forest (65.4%) and XGBoost (23.1%), predominated, while deep learning approaches were less frequent (15.4%). Key predictors included duration on ART (61.5%), CD4 count (69.2%), adherence history (57.7%), age (73.1%) and sex (65.4%). Reported discrimination varied widely (area under the curve [AUC] 0.56-0.99), but interpretation is limited by poor calibration, limited external validation and potential overfitting. Most studies showed a high or unclear risk of bias. Validation practices were limited: 80.8% relied solely on internal validation, 19.2% performed external validation and 30.8% reported calibration assessment. Reporting completeness was also suboptimal, with frequent omissions of confidence intervals and model specifications, indicating incomplete adherence to TRIPOD. No study fulfilled all clinical informatics domains, highlighting a persistent gap between model development and real-world implementation. Longitudinal modelling was limited (15.4%), and random data splitting in such datasets introduced a risk of data leakage.
CONCLUSIONS
Although AI models demonstrate promising predictive performance, their clinical applicability is limited by substantial methodological limitations, including high risk of bias, inadequate validation, poor calibration and limited transparency. Strengthening external and temporal validation, routine calibration, TRIPOD-compliant reporting and integration into clinical workflows will be essential to support reliable deployment, particularly in resource-limited settings.
B. Dejene, Yaregal Assabie, Mulugeta Tadele et al.· HIV Medicine· 0 citations
Background Viral non-suppression is the primary actionable risk state in routine HIV care, yet most individuals are identified after virological failure and/or drug resistance, rather than proactively. In Uganda and similar resource-limited settings, routine electronic medical records (EMR) are collected at scale but remain underused for targeted, data-enabled risk stratification. We aimed to develop and internally validate machine learning and regularized regression models for predicting viral non-suppression using routine monitoring data. Methods We developed and internally validated prediction models for viral non-suppression (viral load ≥1,000 copies/mL) using routinely recorded EMR variables from the TASO Uganda open cohort (2014–2024; n = 33,384). Twenty variables were used across four models: logistic regression (LR), elastic net regularized logistic regression (ENET), random forest (RF), and extreme gradient boosting (XGB), evaluated on a stratified 80:20 test set. Precision-recall AUC (PR-AUC) was the primary metric; ROC-AUC, Brier score, and decision curve analysis were assessed; bootstrap 95% CIs (2,000 replicates) were computed for discrimination metrics. Results On the test set (n = 6,677), RF achieved PR-AUC 0.248 (95% CI 0.207–0.291) and ROC-AUC 0.758 (0.732–0.780); ENET achieved PR-AUC 0.237 (0.198–0.279) and ROC-AUC 0.750 (0.726–0.772); confidence intervals overlapped across all four models. RF and ENET achieved Brier scores of 0.055 (8% below the null of 0.060) and maximum net benefit of 0.055 in decision curve analysis. At the capacity-first threshold (top 5% predicted risk), RF flagged 325 individuals (4.9%; PPV 0.338, NPV 0.949) and ENET flagged 334 (5.0%; PPV 0.305, NPV 0.948). Current ART class (PI-based: OR 11.07; NNRTI-based: OR 4.80), poor adherence (OR 7.71), TB history (OR 2.08), and male sex (OR 1.62) were the strongest predictors. Conclusions Routine EMR data support meaningful, calibrated viral non-suppression risk prediction across a large, multi-site Ugandan HIV program. At a capacity-first threshold, both models achieved approximately five-fold enrichment over background prevalence, with clinical utility confirmed by decision curve analysis. Prospective external validation and workflow integration are required before deployment.
Maria Magdalene Namaganda, Stathis Gennatas, L. Merson et al.· Frontiers in Artificial Inte...· 0 citations
The early prediction of opportunistic infections (OIs) among people living with HIV (PLWH). Despite the widespread use of antiretroviral therapy (ART), opportunistic infections remain a major cause of morbidity and mortality, particularly in low- and middle-income settings. Early identification of individuals at high risk is therefore essential for improving clinical outcomes and optimizing healthcare resource allocation. comprehensive dataset comprising 3,982 patient records and 88 clinical, demographic, laboratory, and treatment-related features was utilized. The dataset included categorical, numerical, and text-based variables capturing diverse aspects of patient health status. Principal Component Analysis (PCA) was applied for dimensionality reduction, identifying key contributing factors such as current weight, age, ART refill patterns, and engagement in enhanced adherence counseling sessions. Several machine learning algorithms were explored, with XGBoost emerging as the best-performing model. The model achieved an accuracy of 97.17%, with an AUC of 0.994 and an Average Precision score of 0.979, demonstrating strong discriminative ability. Evaluation metrics further confirmed sensitivity and specificity, with minimal false negatives, which is critical for clinical safety in HIV care.The proposed framework demonstrates the potential of machine learning to support early risk stratification and clinical decision-making for opportunistic infections in HIV-positive populations.
Udoinyang G. Inyang, Abraham C. Inyang, Emmanuel A. Ubong et al.· International Journal of Lat...· 0 citations
Maternal mental health disorders remain a major public health problem, particularly in low- and middle-income countries, where limited mental health resources and inadequate screening systems lead to late diagnosis and intervention. Recent advances in artificial intelligence have demonstrated great potential in improving the prediction of early maternal mental health risks. However, most current methods are limited to traditional machine learning models and lack interpretability. This study introduces the explainable Hybrid Classical-Quantum Support Vector Machine (SVM) framework for the prediction of maternal mental health risk using multicountry psychosocial and clinical data from Uganda and Pakistan. The proposed framework integrates classical SVM learning with quantum-enhanced feature representations, while SHapley Additive exPlanations (SHAP) are leveraged to increase the model transparency and clinical interpretability. A comparative evaluation was performed against Random Forest, XGBoost, LightGBM, CatBoost, Stacking Ensemble, Classical SVM, and standalone Quantum SVM using Accuracy, Precision, Recall, F1-score, Receiver Operating Characteristic Area Under the Curve (ROC-AUC) and Precision-Recall Area Under the Curve (PR-AUC). We performed two experimental settings: one using the full set of features and another using a set of features without leakage, i.e. excluding variables that directly contribute to the EPDS-derived outcome. Under the complete feature set, the proposed Hybrid Classical-Quantum SVM achieved 99.86% accuracy, 99.80% F1-score, 99.92% ROC-AUC and 99.91% PR-AUC, while maintaining competitive performance after the leakage-free evaluation. The SHAP analysis provided enhanced model interpretability, identifying clinically meaningful demographic, obstetric, socioeconomic and psychosocial factors associated with maternal mental health risk. Our results demonstrate that hybrid classical-quantum learning offers a competitive and explainable paradigm for predicting maternal mental health risk and highlights the complementary nature of quantum feature representations in healthcare analytics.
Shallon Ahimbisibwe, Emmanuel Ahishakiye, S. Maling et al.· Discover Artificial Intellig...· 0 citations
A hybrid framework for a comprehensive evaluation of machine learning and transformer-based models for cardiovascular risk prediction by integrating structured clinical data with unstructured clinical narratives is included in this study. The structured data includes demographic and diagnostic variables like patient information and test results, while unstructured data is derived from electronic health records such as clinical notes. Through preprocessing, feature engineering, and semantic embedding using transformer-based models, the system leverages the complementary strengths of both data types. By analysing and processing this unstructured information, this project aims to improve the predictions for heart disease. The results show that it is feasible to make better predictions and gain valuable insights by merging these two types of data. Such analysis is essential because structured data alone often overlooks fine-grained clinical indicators found in narrative texts. Integrating unstructured data allows for a more holistic view of patient health, leading to earlier detection, personalized interventions, and improved decision-making in clinical settings.