Optimizing and validating a pediatric screening tool to more efficiently test and identify children living with HIV

Joseph, Jessica T; Mpasela, Felton; Dowling, Stephanie; Banda, Isaac; Bobo, Patricia M; Carmone, Andy E; Haimbe, Prudence; Hasweeka, Pamfred; Kampekete, Gertrude S; Mumba, Fredrick; Mwanza, Felix; Sakulanda, Charles; Simasiku, Mwanei; Suggu, Kanchana; Shakwelele, Hilda; Munthali, Gloria

doi:10.1097/qad.0000000000003583

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the decision curves, the NN model had the larger net gain than other models after intervention. Comprehensive assessment, instead of using only one indicator such as the AUC (41,42), was also used in this study, which helped find the optimal model, NN model, for prolonged hospital stay (43).…”

Section: Discussionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%