Using machine learning to develop a clinical prediction model for SSRI-associated bleeding: a feasibility study.

ABSTRACT:

Introduction

Adverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine learning (ML) to predict selective serotonin reuptake inhibitor (SSRI)-associated bleeding.

Methods

The AoU program, beginning in 05/2018, continues to recruit ≥ 18 years old individuals across the United States. Participants completed surveys and consented to contribute electronic health record (EHR) for research. Using the EHR, we determined participants who were exposed to SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine). Features (n = 88) were selected with clinicians' input and comprised sociodemographic,

SUBMITTER: Goyal J 

PROVIDER: S-EPMC10257821 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature