Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence

Kamal, Susan; Urata, John; Cavassini, Matthias; Liu, Honghu; Kouyos, Roger D.; Bugnon, Olivier; Wang, Wei; Schneider, Marie‐Paule

doi:10.1080/09540121.2020.1751045

Cited by 15 publications

(12 citation statements)

References 18 publications

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“… 39 Recently, Kamal et al conducted a study employing RF-based prediction of virologic outcomes in Switzerland, which achieved an AUC of 0.77. 38 Meanwhile, our accuracy finding is in agreement with a study of Bisaso et al, which reports a predictive modeling accuracy of 92.9%. 36 Overall, our XGB model shows good predictive performance in various diagnostic metrics, such as accuracy, precision, sensitivity, and F1 score.…”

Section: Discussionsupporting

confidence: 92%

“… 38 , 39 , 52 Furthermore, duration on ART was an important feature for predicting virological failure. Participants who had a longer duration of stay on ART had a higher risk of developing virological failure; this is consistent with the previous studies, 38 , 39 which reported longer ratio of follow-up increases the chances of developing virological failure. Our finding indicated participants who had low CD4 counts had higher chance of virological failure.…”

Section: Discussionmentioning

confidence: 99%

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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

Seboka¹,

Yehualashet²,

Tesfa³

2023

IJGM

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Background Despite the success made in scaling up HIV treatment activities, there remains a tremendous unmet demand for the monitoring of the disease progression and treatment success, which threatens HIV/AIDS treatment and control. This research presented the assessments of viral load and CD4 classification of adults enrolled in ART care using machine learning algorithms. Methods We trained, validated, and tested eight machine learning (ML) classifier algorithms with historical data, including demographics, clinical, and laboratory data. Data were extracted from the ART registry database of Yirgacheffe Primary Hospital and Dilla University Referral Hospital. ML classifiers were trained to predict virological failure (viral load >1000 copies/mL) and poor CD4 (CD4 cell count <200 cells/mL). The model predictive performances were evaluated using accuracy, sensitivity, specificity, precision, f1-score, F-beta scores, and AUC. Results The mean age of the sample participants was 41.6 years (SD = 10.9). The experimental results showed that XGB classifier ranked as the best algorithm for viral load prediction in terms of sensitivity (97%), f1-score (96%), AUC (0.99), accuracy (96%), followed by RF. The GB classifier exhibited a better predictive capability in predicting participants with a CD4 cell count <200 cells/mL. Conclusion In this study, the XGB and RF models had the highest accuracy and outperformed on various evaluation metrics among the models examined for viral load classification. In the prediction of participants CD4, GB model had the highest accuracy.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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

Seboka¹,

Yehualashet²,

Tesfa³

2023

IJGM

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“…In particular, it has been actively used and shown great accomplishment in many biomedical applications. For example, RF has been successfully utilized in cancer prognosis and diagnosis [4,5], to predict infectious diseases with high accuracy [6,7], and to recognize disease associated genes in microarray data [8,9].…”

Section: Introductionmentioning

confidence: 99%

Optimal Feature Set Size in Random Forest Regression

Han

Kim

2021

Applied Sciences

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One of the most important hyper-parameters in the Random Forest (RF) algorithm is the feature set size used to search for the best partitioning rule at each node of trees. Most existing research on feature set size has been done primarily with a focus on classification problems. We studied the effect of feature set size in the context of regression. Through experimental studies using many datasets, we first investigated whether the RF regression predictions are affected by the feature set size. Then, we found a rule associated with the optimal size based on the characteristics of each data. Lastly, we developed a search algorithm for estimating the best feature set size in RF regression. We showed that the proposed search algorithm can provide improvements over other choices, such as using the default size specified in the randomForest R package and using the common grid search method.

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“…In comparison, a machine learning analysis of complex EHR and linked geospatial data to predict dropping out of HIV care (a more rare outcome) in the United States achieved a mean PPV of 35% for identifying PLOS GLOBAL PUBLIC HEALTH the 10% highest-risk individuals [22]. In Switzerland, using EHR data including electronically monitored ART adherence to predict virologic outcomes achieved a PPV of 85% [23]. Our analysis suggests that comparable results to those obtained with detailed data from highincome countries can also be achieved with current EMR data from LMICs.…”

Section: Plos Global Public Healthmentioning

confidence: 99%

Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania

Fahey

Wei

Njau

et al. 2022

PLOS Glob Public Health

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Machine learning methods for health care delivery optimization have the potential to improve retention in HIV care, a critical target of global efforts to end the epidemic. However, these methods have not been widely applied to medical record data in low-and middleincome countries. We used an ensemble decision tree approach to predict risk of disengagement from HIV care (missing an appointment by �28 days) in Tanzania. Our approach used routine electronic medical records (EMR) from the time of antiretroviral therapy (ART) initiation through 24 months of follow-up for 178 adults (63% female). We compared prediction accuracy when using EMR-based predictors alone and in combination with sociodemographic survey data collected by a research study. Models that included only EMR-based indicators and incorporated changes across past clinical visits achieved a mean accuracy of 75.2% for predicting risk of disengagement in the next 6 months, with a mean sensitivity of 54.7% for targeting the 30% highest-risk individuals. Additionally including survey-based predictors only modestly improved model performance. The most important variables for prediction were time-varying EMR indicators including changes in treatment status, body weight, and WHO clinical stage. Machine learning methods applied to existing EMR data in resource-constrained settings can predict individuals' future risk of disengagement from HIV care, potentially enabling better targeting and efficiency of interventions to promote retention in care.

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Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence

Cited by 15 publications

References 18 publications

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

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

Optimal Feature Set Size in Random Forest Regression

Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania

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