Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data
Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data
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DOI：
10.1177/1460458217751015
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发表时间：
2019-12-01
影响因子：
3
通讯作者：
Agrawal, Rupesh K.
中科院分区：
文献类型：
--
作者：
Reddy, Bhargava K.;Delen, Dursun;Agrawal, Rupesh K.
克罗恩病是影响胃肠道的慢性炎症性肠病之一。实时了解和预测炎症的严重程度对于疾病管理至关重要。现有文献主要集中在临床试验环境中进行的研究，以调查药物治疗对疾病缓解状态的影响。这项研究提出了一种分析方法，其中开发了三种不同类型的预测模型来预测和解释诊断为克罗恩病的患者炎症的严重程度。结果表明，基于机器学习的分析方法（例如梯度增强机）可以以非常高的准确度预测炎症严重程度（曲线下面积=92.82%），然后进行正则化回归和逻辑回归。根据研究结果，基线实验室参数、患者人口统计特征和疾病部位的组合是克罗恩病患者炎症严重程度的最强预测因素。
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.