Abstract
| Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data Reddy BK1, Delen D1, Agrawal RK1.Author informationHealth Informatics J. 2018 Jan 1:1460458217751015. doi: 10.1177/1460458217751015. [Epub ahead of print] |
| 1 Oklahoma State University, USA. 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 diseasemanagement. 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. |
© Copyright 2013-2025 GI Health Foundation. All rights reserved.
This site is maintained as an educational resource for US healthcare providers only.
Use of this website is governed by the GIHF terms of use and privacy statement.
