Clinical characteristics and prognostic factors for Crohn's disease relapses using natural language processing and machine learning: a pilot study.

ABSTRACT:

Background

The impact of relapses on disease burden in Crohn's disease (CD) warrants searching for predictive factors to anticipate relapses. This requires analysis of large datasets, including elusive free-text annotations from electronic health records. This study aims to describe clinical characteristics and treatment with biologics of CD patients and generate a data-driven predictive model for relapse using natural language processing (NLP) and machine learning (ML).

Methods

We performed a multicenter, retrospective study using a previously validated corpus of CD patient data from eight hospitals of the Spanish National Healthcare Network from 1 January 2014 to 31 December 2018 using NLP. Predictive models were created with ML algorithms, namely, logistic regression, dec

SUBMITTER: Gomollon F 

PROVIDER: S-EPMC8876385 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature