Ahmed Mohamed, Hala Elhoweris
Perceptions of preschool teachers of the characteristics of gifted learners in Abu Dhabi: A qualitative study
Frontiers in psychology
posterior circulation syndrome pcs presents diagnostic challenge characterized variable nonspecific symptoms timely accurate diagnosis crucial improving patient outcomes study aims enhance early diagnosis pcs employing clinical demographic data machine learning approach targets significant research gap field stroke diagnosis management collected analyzed data large national stroke registry spanning january 2014 july 2022 dataset included 15859 adult patients admitted primary diagnosis stroke five machine learning models trained xgboost random forest support vector machine classification regression trees logistic regression multiple performance metrics accuracy precision recall f1score auc matthews correlation coefficient log loss brier score utilized evaluate model performance xgboost model emerged top performer auc 081 accuracy 079 precision 05 recall 062 f1score 055 shap shapley additive explanations analysis identified key variables associated pcs including body mass index random blood sugar ataxia dysarthria diastolic blood pressure body temperature variables played significant role facilitating early diagnosis pcs emphasizing diagnostic value study pioneers use clinical data machine learning models facilitate early diagnosis pcs filling crucial gap stroke research using simple clinical metrics bmi rbs ataxia dysarthria dbp body temperature help clinicians diagnose pcs early despite limitations data biases regional specificity research contributes advancing pcs understanding potentially enhancing clinical decisionmaking patient outcomes early patients clinical journey investigations warranted elucidate underlying physiological mechanisms validate findings broader populations healthcare settings