开发和验证用于炎症性肠病诊断的预测模型:一种基于机器学习和名图的方法
Rongrong Dong1, Yiting Wang2, Han Yao1
1Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
Journal of inflammation research
|April 21, 2025
概括
这项研究开发了机器学习和名录模型,使用实验室数据来预测炎症性肠病 (IBD),克罗恩病 (CD) 和性结肠炎 (UC). 这些模型显示出良好的诊断能力,为IBD诊断提供了一种新方法.
科学领域:
- 胃肠病学和肝病学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 炎症性肠病 (IBD) 包括慢性,无法治愈的胃肠道疾病,如克罗恩病 (CD) 和性结肠炎 (UC).
- 目前IBD的诊断方法缺乏明确的黄金标准,需要改进的预测工具.
研究的目的:
- 开发和验证用于诊断IBD,CD和UC的预测模型.
- 在IBD诊断中比较机器学习 (ML) 和传统的名录模型的有效性.
主要方法:
- 利用了来自英国生物银行,吉林大学第一医院和中国三级医院的初步实验室测试数据的三个队列.
- 使用LightGBM和XGBoost算法开发了ML模型,并通过后勤回归开发了名ogram模型.
- 在独立队列中使用曲线下的面积 (AUC) 度量来验证模型性能.
主要成果:
- 机器学习模型展示了跨队列的异常歧视,AUC在CD和UC预测中从0.772到0.932不等.
- 诺莫图模型表现出良好的诊断能力,在外部测试队列中,IBD,CD和UC的验证AUC从0.758到0.817不等.
- 两种ML和名图方法都显示出IBD亚型的有希望的预测性能.
结论:
- 开发了IBD,CD和UC的风险预测模型,使用传统的实验室数据和集成的ML和名ogram技术.
- 这些模型表现出良好的诊断能力,并在独立的队列中成功验证,这表明它们具有临床实用性.
- 这项研究提供了一种新的,数据驱动的方法,以提高炎症性肠道疾病的诊断准确度.
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