Jatin Goyal|Ding Quan Ng|Kevin Zhang|Alexandre Chan|Joyce Lee|Kai Zheng|Keri Hurley?Kim|Lee S. Nguyen|Lu He|Megan Nguyen|Sarah McBane|Wei Li|Christine Cadiz
Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study
University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine|University of California, Irvine
abstract introduction 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 methods 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 gt 001 decline auc removal model three four ml models results 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 conclusions demonstrated feasibility predicting ades using ml incorporating genomic features drug interactions deep learning models may improve ade prediction