Noy Alon, Sarah Perret, Amy Cohen, Michelle Partiquin, Stephanie LeMelle, Chasee Boyd, Jacqueline Aguilera, Cristen Reat, Elise Hough, Jessica Walsh, Bridget Dwyer, Julianna Hogan, Ashley Helm Smith, John Torous
Digital Navigator Training to Increase Access to Mental Health Care in Community-Based Organizations
Psychiatric services (Washington, D.C.)
chronic subdural hematoma csdh neurological condition high recurrence rates primarily observed elderly population although several risk factors identified predicting csdh recurrence remains challenge given potential machine learning ml extract meaningful insights complex data sets study aims develop validate ml models capable accurately predicting postoperative csdh recurrence data 447 csdh patients treated consecutive burrhole irrigations wenzhou medical universitys first affiliated hospital december 2014april 2019 studied 312 patients formed development cohort 135 comprised test cohort least absolute shrinkage selection operator lasso method employed select crucial features associated recurrence eight machine learning algorithms used construct prediction models hematoma recurrence using demographic laboratory radiological features borderline synthetic minority oversampling technique smote applied address data imbalance shapley additive explanation shap analysis utilized improve model visualization interpretability model performance assessed using metrics auroc sensitivity specificity f1 score calibration plots decision curve analysis dca optimized ml models exhibited prediction accuracies ranging 610 862 hematoma recurrence validation set notably random forest rf model surpassed algorithms achieving accuracy 862 shap analysis confirmed results highlighting key clinical predictors csdh recurrence risk including age alanine aminotransferase level fibrinogen level thrombin time maximum hematoma diameter rf model yielded accuracy 926 auc value 0834 test dataset findings underscore efficacy machine learning algorithms notably integration rf model smote forecasting recurrence postoperative chronic subdural hematoma leveraging rf model devised online calculator may serve pivotal instrument tailoring therapeutic strategies implementing timely preventive interventions highrisk patients