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基于机器学习算法的预测模型的构建和验证,用于难以插入结肠镜的难以插入的结肠镜

Ren-Xuan Gao¹, Xin-Lei Wang², Ming-Jie Tian³

¹Department of Gastroenterology, North China University of Science and Technology Affiliated Hospital, Tangshan 063000, Hebei Province, China.

World journal of gastrointestinal endoscopy

|July 18, 2025

概括

机器学习模型可以预测结肠镜插入的难度 (DCI). 随机森林 (RF) 模型显示出最好的准确性,确定便秘,腹周和焦虑是改善患者护理的关键风险因素.

科学领域:

胃肠病学胃肠病学
医疗信息学医疗信息学
机器学习机器学习

背景情况:

结肠镜插入的困难 (DCI) 是一个重大的挑战,影响了手术的有效性和质量.
在手术前预测DCI风险对于优化手术内策略和改善患者结果至关重要.

研究的目的:

为了比较机器学习 (ML) 算法对DCI的预测性能.
确定影响DCI的关键因素.
开发一个基于ML的DCI预测模型,以提高结肠镜检查的质量.

主要方法:

一项涉及712名接受结肠镜检查的患者的横截面研究.
收集的数据包括人口统计,病史,药物和心理状态.
使用多变量逻辑回归,LASSO回归和随机森林 (RF) 算法开发的预测模型.
使用歧视,校准和决策曲线分析 (DCA) 评估模型性能.

主要成果:

便秘,腹周和焦虑被确定为DCI的重要预测因素.
随机森林 (RF) 模型表现出卓越的预测准确性,验证AUC为0.754.
射频模型在训练中实现了高灵敏度 (1000) 和特异性 (0.977),表现优于后勤回归和LASSO模型.

结论:

与传统的回归方法相比,基于射频的模型为DCI提供了更高的预测准确性.
这种ML方法可以实现个性化的手术前风险分层.
开发的模型可以通过有针对性的干预来提高结肠镜检查的质量和效率.

关键词:

结肠镜检查是一次结肠镜检查.结肠镜插入的困难结肠镜插入的困难最小绝对收缩和选择操作员回归.后勤回归的逻辑回归机器学习算法机器学习算法预测模型是一个预测模型.随机的森林随机的森林

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