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使用EHR数据深度表型肥胖:承诺,挑战和未来方向

Xiaoyang Ruan¹, Shuyu Lu¹, Liwei Wang²

¹Department of Health Data Science and AI, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston.

medRxiv : the preprint server for health sciences

|December 16, 2024

概括

电子健康记录 (EHR) 能够对肥胖症进行深度表型识别,识别出个性化医疗的不同患者群. 这种方法揭示了临床相关的子组,为定制的抗肥胖药物 (AOM) 策略铺平了道路.

科学领域:

医疗信息学医学信息学
计算生物学是一种计算生物学.
精准医学是一门精准的医学.

背景情况:

肥胖影响了美国人口的很大一部分,带来了巨大的经济和心理社会挑战.
对抗肥胖药物 (AOM) 的患者反应多样化,需要针对个性化治疗策略进行先进的表型化.
目前的表型化方法往往缺乏广泛临床采用所需的细粒度和操作简单性.

研究的目的:

评估电子健康记录 (EHR) 作为肥胖患者深度表型化数据源的实用性.
探索基于EHR的肥胖症表型的可行性,数据要求,集群模式和挑战,使用多模式纵向深度自动编码器.
根据治疗前的EHR数据识别不同的患者子组,以告知精准医学方法.

主要方法:

分析了32,969名患者的53,688个AOM前期,利用92个实验室/生命测量和79个ICD衍生代码.
应用一个带有衰变 (GRU-D) 的封闭循环单元基于纵向自编码器来生成患者嵌入.
利用主要组件分析 (PCA) 和高斯混合模型 (GMM) 进行集群识别.

主要成果:

确定至少9个不同的患者群,其中5个具有明显的临床相关性.
聚类模式在多个训练折叠中显示出稳定性,具有可重现的临床意义.
发现的挑战包括缺少数据归算稳定性,输入特征一致性和复杂数据的低维可视化.

结论:

纵向EHR数据是AOM前期每次访问深度表型化的宝贵资源.
确定的患者群体表明了针对AOM治疗选择的潜在影响.
需要在更大的队列中进行进一步验证,以确认可重现性,临床相关性,并揭示详细的子结构和治疗反应.

关键词:

抗肥胖药物抗肥胖药物欧洲人权理事会欧洲人权理事会肥胖问题肥胖问题现型化 (Phenotyping) 是一种表现方式.精准医学是一门精准的医学.

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