Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines.

ABSTRACT:

Background and aims

Big data analytics leverage patterns in data to harvest valuable information, but are rarely implemented in clinical care. Optimising thiopurine therapy for inflammatory bowel disease [IBD] has proved difficult. Current methods using 6-thioguanine nucleotide [6-TGN] metabolites have failed in randomized controlled trials [RCTs], and have not been used to predict objective remission [OR]. Our aims were to: 1) develop machine learning algorithms [MLA] using laboratory values and age to identify patients in objective remission on thiopurines; and 2) determine whether achieving algorithm-predicted objective remission resulted in fewer clinical events per year.

Methods

Objective remission was defined as the absence of objective evidence of intestinal inflammat

SUBMITTER: Waljee AK 

PROVIDER: S-EPMC5881698 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature