Peter G Szilagyi, Stanley Schaffer, Cynthia M Rand, Phyllis Vincelli, Ashley Eagan, Nicolas P N Goldstein, A Dirk Hightower, Mary Younge, Aaron Blumkin, Christina S Albertin, Byung-Kwang Yoo, Sharon G Humiston
School-Located Influenza Vaccinations: A Randomized Trial
Pediatrics
predict post transurethral prostate resectionturp urethral stricture probability applying different machine learning algorithms using data obtained preoperative blood parameters retrospective analysis data patients underwent bipolarturp encompassing patient characteristics preoperative routine blood test outcomes postsurgery uroflowmetry used develop educate machine learning models various metrics f1 score model accuracy negative predictive value positive predictive value sensitivity specificity youden index roc auc value confidence interval model used assess predictive performance machine learning models urethral stricture development total 109 patients data 55 patients without urethral stricture 54 patients urethral stricture included study implementing strict inclusion exclusion criteria preoperative platelet distribution width mean platelet volume plateletcrit activated partial thromboplastin time prothrombin time values statistically meaningful two cohorts applying data machine learning systems accuracy prediction scores diverse algorithms follows decision trees 082 logistic regression 082 random forests 091 support vector machines 086 knearest neighbors 082 nave bayes 077 machine learning models accuracy predicting postturp urethral stricture probability demonstrated significant success exploring prospective studies integrate supplementary variables potential enhance precision accuracy machine learning models consequently progressing ability predict postturp urethral stricture risk