通过使用机器学习来确定功能性消化不良患者的模式的关键特征
Heeyoung Moon1,2, Da-Eun Yoon1, Junsuk Kim3
1Acupuncture and Meridian Science Research Center, Kyung Hee University, Seoul, Republic of Korea.
Frontiers in physiology
|October 27, 2025
概括
这项研究使用机器学习来识别功能性消化不良 (FD) 模式识别 (PI) 的关键症状. 它揭示了不同的患者子组,帮助FD的临床决策.
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 模式识别 (PI) 对于理解疾病症状和征兆至关重要.
- 功能性消化不良 (FD) 诊断受益于有效的PI策略.
- 目前的PI方法可能无法捕捉所有患者的异质性.
研究的目的:
- 使用监督学习提取FD中常规PI的特征.
- 通过无监督学习识别新的PI类型和患者子组.
- 为了比较来自FD的监督和无监督方法的特征集.
主要方法:
- 利用来自153名FD患者的问卷数据进行监督学习 (支持矢量机器 - SVM).
- 采用无监督学习 (t-分布式随机邻居嵌入 - t-SNE,k-意味着集群) 来发现患者子组.
- 应用独立样本t测试以确定已识别的集群之间的区分特征.
主要成果:
- SVM将食欲丧失,侧面不适,腹部膨胀/声和肤色变化确定为关键的歧视性特征.
- 无监督聚类显示了基于症状概况的四个不同的FD患者子组.
- 确定的症状集群包括全身症状,腹部上部症状,肠道变动,恶心/吐.
结论:
- 监督学习有效地识别了FD模式识别的关键特征.
- 一种新的无监督学习方法成功地发现了FD内部不同的患者子组.
- 这些发现支持功能性消化不良患者的临床决策.
相关概念视频
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