四种机器学习方法的实用性,用于识别性结肠炎和克罗恩病
Jingwen Pei1, Guobing Wang1, Yi Li1
1Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan 646000, China.
Heliyon
|December 27, 2023
概括
机器学习模型,特别是多层感知人造神经网络 (MLP-ANN),有效地使用周围血液常规参数 (PBRPs) 诊断炎症性肠病 (IBD). 这种方法对区分性结肠炎 (UC) 和克罗恩病 (CD) 有希望.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 周围血液常规参数 (PBRPs) 是炎症性肠病 (IBD) 的可访问标志物,包括性结肠炎 (UC) 和克罗恩病 (CD).
- 个人PBRP对IBD亚型和疾病活动的诊断能力有限.
- 机器学习 (ML) 提供了使用PBRPs增强诊断能力的潜力.
研究的目的:
- 评估四个ML模型的诊断和预测性能,使用PBRPs对UC和CD进行评估.
- 确定最有效的ML模型,以区分IBD亚型和健康对照.
主要方法:
- 一项回顾性研究分析了414名IBD患者,423名健康对照 (HC) 和344名非IBD患者的PBRPs.
- 数据被分为培训 (70%) 和测试 (30%) 集,附加一个外部验证集.
- 接收器运行特征曲线 (AUC) 下的面积用于评估模型性能.
主要成果:
- 多层感知人造神经网络 (MLP-ANN) 模型在各种子组中展示了卓越的诊断性能.
- 在区分IBD与HC (1.00),UC与CD (0.988) 和活跃与缓解的疾病状态方面,MLP-ANN实现了高AUC值.
- 在测试组中,在区分非IBD与HC (0.986) 和IBD与非IBD (0.97) 中也观察到出色的表现.
结论:
- 基于PBRPs的MLP-ANN模型显示了诊断和预测UC和CD以及评估疾病活性的巨大潜力.
- 这种ML方法可以有效地区分IBD与非IBD疾病和健康个体.
- 建议使用更大的样本大小和多样化的模型进行进一步的研究,以验证和扩展这些发现.
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