Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.

ABSTRACT:

Introduction

Previous work had shown that machine learning models can predict inflammatory bowel disease (IBD)-related hospitalizations and outpatient corticosteroid use based on patient demographic and laboratory data in a cohort of United States Veterans. This study aimed to replicate this modeling framework in a nationally representative cohort.

Methods

A retrospective cohort design using Optum Electronic Health Records (EHR) were used to identify IBD patients, with at least 12 months of follow-up between 2007 and 2018. IBD flare was defined as an inpatient/emergency visit with a diagnosis of IBD or an outpatient corticosteroid prescription for IBD. Predictors included demographic and laboratory data. Logistic regression and random forest (RF) models were used to predict

SUBMITTER: Gan RW 

PROVIDER: S-EPMC8452029 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature