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使用机器学习算法,探索刺激性肠综合征的新子组

Elahe Mousavi^1,2, Ammar Hassanzadeh Keshteli², Mohammadreza Sehhati^3,4

¹Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Hezar Jerib Street, PO Box 8174673461, Isfahan, Iran.

Scientific reports

|October 29, 2023

概括

这项研究使用机器学习重新定义了易怒肠综合征 (IBS) 子组,将上肠道症状和心理因素与下肠道症状相结合,以便更好地对患者进行分类.

科学领域:

胃肠病学胃肠病学
精神病学是一个精神病学.
计算生物学计算生物学

背景情况:

刺激性肠综合征 (IBS) 是一种复杂的肠-大脑轴障碍.
目前的IBS分类依赖于下胃肠道症状,忽视心理和上胃肠道因素.
现有的子组可能无法完全捕捉IBS异质性.

研究的目的:

使用无监督机器学习算法重新定义IBS子组.
将上部胃肠道症状和心理负担纳入IBS分类.
为了确定新的,同质的IBS集群.

主要方法:

将混合类型数据聚类算法应用于符合罗马III IBS标准的988名个人.
用一种无监督的机器学习方法来发现子组.
分析了下胃肠道症状,上胃肠道症状和心理负担.

主要成果:

根据症状概况确定了九个不同的IBS子组.
发现了超越传统罗马亚型的新同质集群.
小组在腹,便秘,上部胃肠道症状和心理负担的组合上有所不同.

结论:

机器学习可以通过整合各种症状数据来重新定义IBS子组.
新的集群提供了对IBS异质性的更细致的理解.
这种方法可以改善IBS患者的个性化治疗策略.

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