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基于可解释机器学习方法的炎症性肠病相关睡眠障碍的评估:中国的一项多中心研究

Jiayi Sun¹, Junhai Zhen², Chuan Liu¹

¹Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.

Therapeutic advances in gastroenterology

|August 19, 2025

概括

机器学习有效预测炎症性肠病 (IBD) 患者的睡眠障碍. 关键的危险因素包括抑郁症,焦虑,老年和疾病持续时间,有助于早期干预.

科学领域:

医疗信息学医疗信息学
胃肠病学胃肠病学
睡眠医学睡眠医学

背景情况:

睡眠障碍对炎症性肠病 (IBD) 患者的生活质量产生重大影响.
早期识别和干预睡眠障碍可以改善IBD患者的预后.

研究的目的:

开发和验证一种机器学习 (ML) 风险模型,用于评估IBD患者的睡眠障碍.
确定IBD人口中睡眠障碍的关键预测因素.

主要方法:

一项观察性研究,涉及2478名IBD患者,通过在线调查问卷从中国42家医院招募 (2021年9月 - 2022年5月).
开发和验证6个ML模型来预测睡眠障碍,使用AUC和准确性等指标评估性能.
使用本地可解释模型-不可知论解释 (Lime) 算法进行模型解释.

主要成果:

投票模型表现出卓越的性能,曲线下的面积 (AUC) 为0.76和准确率为0.74.
确定了抑郁症和焦虑症,年龄较大,门诊诊断和更长的疾病持续时间的并发症作为IBD患者睡眠障碍的重要风险因素.

结论:

基于ML的风险评估模型对于预测IBD相关睡眠障碍具有显著的临床价值.
开发的模型显示承诺作为一个有效的临床工具,用于评估IBD患者睡眠障碍风险.

关键词:

人工智能的人工智能是人工智能.炎症性肠病是一种炎症性肠病.机器学习是机器学习.睡眠障碍睡眠障碍睡眠障碍

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