Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

Abstract:

:Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

journal_name

Health Informatics J

authors

Reddy BK,Delen D,Agrawal RK

doi

10.1177/1460458217751015

subject

Has Abstract

pub_date

2019-12-01 00:00:00

pages

1201-1218

issue

4

eissn

1460-4582

issn

1741-2811

journal_volume

25

pub_type

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