首页|Capital Medical University Reports Findings in HIV/AIDS (Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study)
Capital Medical University Reports Findings in HIV/AIDS (Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study)
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New research on Immune System Diseases and Conditions - HIV/AIDS is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “The 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. Regression models were established based on RF, KNN, SVM, and XGB to predict the length of hospital stay using RMSE, MAE, MAPE, and , 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.”
BeijingPeople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesHIV/AIDSHealth and MedicineHospitalsImmune System Diseases and ConditionsMachine LearningPrimate LentivirusesRNA VirusesRetroviridaeRisk and PreventionVertebrate VirusesViral Sexually Transmitted Diseases and Conditions
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