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

Seboka, Binyam Tariku; Yehualashet, Delelegn Emwodew; Tesfa, Getanew Aschalew

doi:10.2147/ijgm.s397031

Cited by 9 publications

(4 citation statements)

References 56 publications

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“…Seboka Binyam Tariku et al used machine learning algorithms to evaluate viral load and CD4 classi cation in adults attending ART care: The experimental results showed that the XGboost classi er was the best algorithm for viral load prediction in terms of sensitivity (97%), f1-score (96%), AUC (0.99), and accuracy (96%), followed by the RF model. Therefore, it can be concluded that the XGboost model has some application value [24] . Some studies have also used machine learning models to predict the spread of HIV in the AIDS pandemic, and the collected research factors are similar to those in this study, such as patient age, marital status, type of residence, occupation and route of HIV infection.…”

Section: Discussionmentioning

confidence: 99%

Classification and prediction of pulmonary tuberculosis infection in HIV/AIDS patients based on XGboost algorithm

Yuan,

Tuersun,

You

et al. 2024

Preprint

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Background Describe the current epidemic situation of HIV/AIDS complicated with pulmonary tuberculosis (PTB) infection in Kashgar region, analyze the factors affecting HIV/AIDS complicated with PTB infection, and use XGboost model to classify and predict the risk of PTB infection in HIV/AIDS patients, so as to improve the level of protection of residents. Methods By collecting the data of HIV/AIDS patients in Kashgar area, the patients were divided into simple HIV/AIDS group and double infection group according to whether they had PTB. The prevalence of patients with double infection was described and the influencing factors of double infection were analyzed. All study subjects were divided into a train set and a test set with a ratio of 8:2. Linear models penalized with the L1 norm was selected as the feature selection method. With XGBoost and logistic regression algorithms, prediction models for the risk of PTB infection in HIV/AIDS patients were constructed. ROC curve, delong test, decision curve and calibration curve were used to evaluate the model effect. Results The PTB infection rate among HIV/AIDS patients was 33.6%. Residing in Kashgar, initial CD4 lymphocyte count < 200 cells/mm3, and white blood cell count of 4 ~ 10×10^9/L and > 10×10^9/L were associated with an increased risk of PTB in HIV/AIDS patients, whereas being a worker or farmer and having WHO clinical stage II were protective factors. The logistic regression model achieved an AUC of 0.6962 on the training set and 0.6681 on the test set, while the XGBoost model had an AUC of 0.9027 on the training set and 0.8026 on the test set. The Delong test P-values for the training set and test set were < 0.001 and 0.009, respectively, indicating superior predictive performance of the XGBoost model. Conclusions The dual infection rate of HIV/AIDS patients with PTB in Kashgar is high, and timely intervention should be performed for HIV/AIDS patients living in Kashgar with initial CD4 lymphocyte count < 200 cells/mm3, white blood cell count of 4 ~ 10×10^9/L and > 10×10^9/L. Moreover the XGboost model has better predictive efficacy than the logistic regression model, indicating that it can be used to classify and predict PTB infection in HIV/AIDS patients.

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Section: Discussionmentioning

confidence: 99%

Classification and prediction of pulmonary tuberculosis infection in HIV/AIDS patients based on XGboost algorithm

Yuan,

Tuersun,

You

et al. 2024

Preprint

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“…For the training set, 10-fold cross validation and grid search was used to obtain the best model hyper-parameters, which were used into four ML regression model algorithms, including random forest (RF), k-Nearest Neighbor (KNN), support vector machine (SVM), and extreme gradient boosting (XGB) (25,26) to predict the length of hospital stay in PLWHs. The root mean squard error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and R-squared (R 2 ) were calculated respectively to evaluate the performance of different regression models.…”

Section: Predicting Individual Length Of Hospital Stay Based On ML Re...mentioning

confidence: 99%

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study

Li,

Hao,

Liu

et al. 2024

Front. Public Health

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ObjectiveThe study aimed to use supervised machine learning models to predict the length and risk of prolonged hospitalization in PLWHs to help physicians timely clinical intervention and avoid waste of health resources.MethodsRegression models were established based on RF, KNN, SVM, and XGB to predict the length of hospital stay using RMSE, MAE, MAPE, and R2, while classification models were established based on RF, KNN, SVM, NN, and XGB to predict risk of prolonged hospital stay using accuracy, PPV, NPV, specificity, sensitivity, and kappa, and visualization evaluation based on AUROC, AUPRC, calibration curves and decision curves of all models were used for internally validation.ResultsIn regression models, XGB model performed best in the internal validation (RMSE = 16.81, MAE = 10.39, MAPE = 0.98, R2 = 0.47) to predict the length of hospital stay, while in classification models, NN model presented good fitting and stable features and performed best in testing sets, with excellent accuracy (0.7623), PPV (0.7853), NPV (0.7092), sensitivity (0.8754), specificity (0.5882), and kappa (0.4672), and further visualization evaluation indicated that the largest AUROC (0.9779), AUPRC (0.773) and well-performed calibration curve and decision curve in the internal validation.ConclusionThis study showed that XGB model was effective in predicting the length of hospital stay, while NN model was effective in predicting the risk of prolonged hospitalization in PLWH. Based on predictive models, an intelligent medical prediction system may be developed to effectively predict the length of stay and risk of HIV patients according to their medical records, which helped reduce the waste of healthcare resources.

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“…Lastly, advances in computational modelling, artificial intelligence, and machine learning are being harnessed to accelerate the discovery and optimization of new HIV drugs [ 176 , 177 , 178 , 179 , 180 , 181 ]. These technologies enable the rapid screening of large compound libraries, the prediction of drug–target interactions, and the design of novel drug candidates with improved properties.…”

Section: Future Perspectivesmentioning

confidence: 99%

Current ARTs, Virologic Failure, and Implications for AIDS Management: A Systematic Review

Foka

Mufhandu

2023

Viruses

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Antiretroviral therapies (ARTs) have revolutionized the management of human immunodeficiency virus (HIV) infection, significantly improved patient outcomes, and reduced the mortality rate and incidence of acquired immunodeficiency syndrome (AIDS). However, despite the remarkable efficacy of ART, virologic failure remains a challenge in the long-term management of HIV-infected individuals. Virologic failure refers to the persistent detectable viral load in patients receiving ART, indicating an incomplete suppression of HIV replication. It can occur due to various factors, including poor medication adherence, drug resistance, suboptimal drug concentrations, drug interactions, and viral factors such as the emergence of drug-resistant strains. In recent years, extensive efforts have been made to understand and address virologic failure in order to optimize treatment outcomes. Strategies to prevent and manage virologic failure include improving treatment adherence through patient education, counselling, and supportive interventions. In addition, the regular monitoring of viral load and resistance testing enables the early detection of treatment failure and facilitates timely adjustments in ART regimens. Thus, the development of novel antiretroviral agents with improved potency, tolerability, and resistance profiles offers new options for patients experiencing virologic failure. However, new treatment options would also face virologic failure if not managed appropriately. A solution to virologic failure requires a comprehensive approach that combines individualized patient care, robust monitoring, and access to a range of antiretroviral drugs.

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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

Cited by 9 publications

References 56 publications

Classification and prediction of pulmonary tuberculosis infection in HIV/AIDS patients based on XGboost algorithm

Classification and prediction of pulmonary tuberculosis infection in HIV/AIDS patients based on XGboost algorithm

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study

Current ARTs, Virologic Failure, and Implications for AIDS Management: A Systematic Review

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