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在拉丁语IBD组中,用机器学习的临床和遗传变量预测炎症性肠病的肠外表现
Tamara Pérez-Jeldres1,2, Paula Reyes-Pérez3, Patricio Gonzalez-Hormazabal4
1Departmento de Gastroenterología, Pontificia University Católica de Chile, Santiago 8330024, Chile.
肠外表现 (EIM) 影响了近三分之一的炎症性肠病 (IBD) 患者. 遗传因素,家族史,年龄和抗TNF疗法是关键的风险因素,机器学习模型有效预测EIM发展.
科学领域:
- 胃肠病学 胃肠病学
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
背景情况:
- 肠外表现 (EIM) 是炎症性肠病 (IBD) 的常见并发症,增加了患者的发病率.
- 识别风险因素和预测EIM发展对于管理IBD患者至关重要.
研究的目的:
- 在拉丁IBD队列中调查与EIM相关的临床和遗传因素.
- 开发和评估用于预测IBD患者EIM的机器学习模型.
主要方法:
- 分析了414名IBD患者的临床数据 (314名性结肠炎,100名克罗恩病患者).
- 对232名患者进行基因分析,以确定与EIM相关的变异,并计算多基因风险得分 (PRS).
- 在EIM预测中应用后勤回归 (LR),随机森林 (RF) 和梯度增强 (GB) 模型.
主要成果:
- 在IBD队列中,EIM存在于29%的IBD队列中.
- 高龄,IBD家族病史和抗TNF疗法使用与EIM有显著的相关性.
- 四种基因变异 (rs9936833,rs4410871,rs3132680,rs3823417) 与EIM风险有关;LR,RF和GB模型显示出良好的预测性能.
结论:
- 在IBD患者中,EIMs很普遍,受到临床和遗传因素的组合的影响.
- 机器学习模型为预测IBD中EIM风险提供了有效的工具,有助于个性化患者管理.
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