基于机器学习的现象组和功能性胃肠道疾病患者的预测模型,以揭示与气体生产相关的不同疾病子集
Lingling Zhu1,2, Shuo Xu3, Huaizhu Guo1,2
1Department of Gastroenterology, Peking University Third Hospital, Beijing 100191, China.
Journal of translational internal medicine
|October 3, 2024
概括
机器学习通过整合症状和呼吸测试来识别功能性胃肠道疾病 (FGID) 的不同亚组. 这种方法有助于识别过度生产天然气的FGID,以便更好地做出处理决策.
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 微生物组研究 微生物组研究
背景情况:
- 功能性胃肠道疾病 (FGIDs) 的基于症状的分类在揭示潜在机制和指导治疗方面存在局限性.
- 小肠失生症被怀疑是FGID发展的原因之一.
- 需要新的方法来根据病理生理学来分类FGID.
研究的目的:
- 调查机器学习 (ML) 算法是否可以通过将胃肠道 (GI) 症状数据与乳糖呼吸测试结果集成来创建不同的患者群.
- 开发和验证基于ML的预测模型,用于识别FGID患者的过度气体产生.
主要方法:
- 一个有胃肠道症状的508名患者的前性队列接受了乳糖甲和硫化呼吸测试.
- 一个无监督的ML算法 (K-means集群) 整合了胃肠道症状和呼吸测试数据来对患者进行分类.
- 通用估计方程 (GEE) 模型分析了集群和呼吸气体概况之间的纵向关联. 开发并验证了过度气体生产的ML预测模型.
主要成果:
- 通过K-means集群,确定了4个不同的FGID患者集群.
- 集群2,3,4与腹部膨胀,腹和高气体产生有关,而集群1显示下腹部不适,心理抱怨和低气体产生.
- GEE模型证实了跨集群的时间变化的呼吸气体度. ML预测模型在识别过度天然气生产方面表现出很好的准确性.
结论:
- 基于ML的现象组提供了一个强大的方法来创建独特的FGID子集.
- 这些ML方法可以有效地识别FGID子集,其特点是气体产量增加,改善临床决策和治疗指导.
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