Anupama Ivaturi, Loc Giang Do, Lynne Giles, Gemma Devenish, Jane A Scott, Diep H Ha, Rebecca Golley, Lucy K Bell, Paula J Moynihan
Sources and determinants of free sugars intake by 5-year-old Australian children in the SMILE cohort
Maternal & child nutrition
adverse drug events ades associated poor outcomes increased costs may prevented prediction tools national institute health us aou database employed machine learning ml predict selective serotonin reuptake inhibitor ssriassociated bleeding aou program beginning 052018 continues recruit 18 years old individuals across united states participants completed surveys consented contribute electronic health record ehr research using ehr determined participants exposed ssris citalopram escitalopram fluoxetine fluvoxamine paroxetine sertraline vortioxetine features n 88 selected clinicians input comprised sociodemographic lifestyle comorbidities medication use information identified bleeding events validated ehr algorithms applied logistic regression decision tree random forest extreme gradient boost predict bleeding ssri exposure assessed model performance area receiver operating characteristic curve statistic auc defined clinically significant features resulting 001 decline auc removal model three four ml models 10362 participants exposed ssris 96 experiencing bleeding event ssri exposure ssri performance across four ml models relatively consistent aucs best models ranged 06320698 clinically significant features included health literacy escitalopram bleeding history socioeconomic status ssris demonstrated feasibility predicting ades using ml incorporating genomic features drug interactions deep learning models may improve ade prediction