Comparison of machine learning methods for predicting viral failure: a case study using electronic health record data

Kimaina, Allan; Dick, Jonathan; DeLong, Allison; Chrysanthopoulou, Stavroula A.; Kantor, Rami; Hogan, Joseph W.

doi:10.1515/scid-2019-0017

Cited by 2 publications

(4 citation statements)

References 24 publications

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“…They found out that decision trees (C4.5) performed the best with model fitness and prediction accuracy of 84.45% and 74.21% respectively. These study findings were similar to studies conducted in Kenya (53) and China (54) that equally reported the decision trees prediction to perform better with an accuracy of 90% and 70.9% respectively. Noteworthy, the study from .…”

Section: Use Of Machine Learning In Healthcaresupporting

confidence: 90%

“…These results indicate that classification machine learning models can be used to map the different predictors to their respective classes. Likewise, these capabilities have been reported in similar rather challenging scenarios like predicting viral failure (53), tweets classification for disease surveillance (59), identification of HIV predictors for screening (60), and dermatology conditions (67). Therefore, this demonstrates the potential and applicability of machine learning algorithms to provide insights in scenarios where human decision-making would be limited.…”

Section: Discussionmentioning

confidence: 59%

“…The copyright holder for this preprint this version posted December 6, 2022. ; https://doi.org/10.1101/2022.12.01.22283003 doi: medRxiv preprint Kenya concluded that ML techniques have the potential to identify patients at risk for viral failure before their scheduled measurements (53).…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

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A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Gichuhi

Magumba

Kumar

et al. 2022

Preprint

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Despite the availability and implementation of well-known efficacious interventions for Tuberculosis (TB) prevention and treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific patient at risk of non-adherence is still a challenge. Thus, this study set out to utilize machine learning modeling to explore individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district. This was a retrospective study based on a record review of 838 TB patients enrolled in six health facilities (3 government, 3 private-not-for-profit) in the Mukono district. We developed five machine learning algorithms (Logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), Random Forest (RF), and AdaBoost) to explore the individual risk factors for tuberculosis treatment non-adherence. Also, we evaluated their accuracy by computing the positive predictive value (PPV) and negative predictive value (NPV) through the aid of a confusion matrix. Of the five developed and evaluated models, SVM performed the best with an accuracy of 91.28 % compared to RF (89.97%), LR (88.30%), ANN (88.30%), and AdaBoost (91.05%) respectively. Individual risk factors predictive of non-adherence included; TB type, GeneXpert results, sub-country, ART status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, CPT Dapson status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 months and 6 months. This study shows that classification machine learning techniques can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, TB program management should consider adopting the machine learning techniques evaluated in this study as a screening tool for both identity and target-suited interventions for these patients.

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Section: Use Of Machine Learning In Healthcaresupporting

confidence: 90%

Section: Discussionmentioning

confidence: 59%

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A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Gichuhi

Magumba

Kumar

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These results indicate that classification machine learning models can be used to map the different predictors to their respective classes. Likewise, these capabilities have been reported in similar rather challenging scenarios like predicting viral failure [ 51 ], tweets classification for disease surveillance [ 43 ], identification of HIV predictors for screening [ 52 ], and cancer [ 53 ]. Therefore, this demonstrates the potential and applicability of machine learning algorithms to provide insights in scenarios where human decision-making would be limited.…”

Section: Discussionmentioning

confidence: 94%

A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Gichuhi

Magumba

Kumar

et al. 2023

PLOS Glob Public Health

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Despite the availability and implementation of well-known efficacious interventions for tuberculosis treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific tuberculosis patient at risk of treatment non-adherence is still a challenge. Thus, this retrospective study, based on a record review of 838 tuberculosis patients enrolled in six health facilities, presents, and discusses a machine learning approach to explore the individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district, Uganda. Five classification machine learning algorithms, logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and AdaBoost were trained, and evaluated by computing their accuracy, F1 score, precision, recall, and the area under the receiver operating curve (AUC) through the aid of a confusion matrix. Of the five developed and evaluated algorithms, SVM (91.28%) had the highest accuracy (AdaBoost, 91.05% performed better than SVM when AUC is considered as evaluation parameter). Looking at all five evaluation parameters globally, AdaBoost is quite on par with SVM. Individual risk factors predictive of non-adherence included tuberculosis type, GeneXpert results, sub-country, antiretroviral status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, cotrimoxazole preventive therapy (CPT) dapsone status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 and 6 months. Therefore, machine learning techniques, specifically classification types, can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, tuberculosis program management should consider adopting the classification machine learning techniques evaluated in this study as a screening tool for identifying and targeting suited interventions to these patients.

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Comparison of machine learning methods for predicting viral failure: a case study using electronic health record data

Cited by 2 publications

References 24 publications

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

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