Mamaru Melkam, Bezawit Melak Fente
Multilevel analysis of discrimination of people living with HIV/AIDS and associated factors in Ghana: demographic health survey of 2022 Ghana data
Frontiers in public health
frequency anterior cervical discectomy fusion acdf increased 400 since 2011 underscoring need preoperatively anticipate adverse postoperative outcomes given procedures expanding use study aims accomplish two goals firstly develop suite explainable machine learning ml models capable predicting adverse postoperative outcomes following acdf surgery secondly embed models userfriendly web application demonstrating potential utility utilized data national surgical quality improvement program database identify patients underwent acdf surgery outcomes interest four shortterm postoperative adverse outcomes prolonged length stay los nonhome discharges 30day readmissions major complications utilized five ml algorithms tabpfn tabnet xgboost lightgbm random forest coupled optuna optimization library hyperparameter tuning bolster interpretability models employed shapley additive explanations shap evaluating predictor variables relative importance used partial dependence plots illustrate impact individual variables predictions generated topperforming models visualized model performance using receiver operating characteristic roc curves precisionrecall curves prc quantitative metrics calculated area roc curve auroc balanced accuracy weighted area prc auprc weighted precision weighted recall models highest auroc values selected inclusion web application analysis included 57760 patients prolonged los 111 prolonged los 57780 nonhome discharges 33 nonhome discharges 57790 30day readmissions 29 readmitted 57800 major complications 14 major complications topperforming models ones built random forest algorithm yielded mean aurocs 0776 0846 0775 0747 predicting prolonged los nonhome discharges readmissions complications respectively study employs advanced ml methodologies enhance prediction adverse postoperative outcomes following acdf designed accessible web application integrate models clinical practice findings affirm ml tools serve vital supplements risk stratification facilitating prediction diverse outcomes enhancing patient counseling acdf