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肥胖的深度表型:基于电子健康记录的时间模型研究
Xiaoyang Ruan1, Shuyu Lu1, Liwei Wang2
1Department of Health Data Science and AI, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, TX, 77030, United States, 1 713-500-3924.
Journal of medical Internet research
|August 20, 2025
概括
电子健康记录 (EHR) 可以用于肥胖深度表型,在治疗抗肥胖药物 (AOM) 之前识别不同的患者群. 这种方法有助于准确医学,因为它揭示了针对定制的AOM策略的临床相关子组.
科学领域:
- 生物医学信息学
- 精准医学
- 肥胖研究
背景情况:
- 美国40%的成年人和15-20%的儿童患有肥胖症,
- 个人对抗肥胖药物 (AOM) 的反应有很大差异.
- 深度表型和精准医学对于有效治疗肥胖至关重要.
研究的目的:
- 评估电子健康记录 (EHR) 作为肥胖深度表型的数据来源.
- 研究基于EHR的深度表型化的可行性,数据要求和集群模式.
- 确定使用EHR数据进行肥胖表型的挑战.
主要方法:
- 对32,969名患者的53,688个AOM前期的分析.
- 使用了92个实验室/生命测量和79个ICD衍生的CCS代码.
- 应用多模式纵向深度自编码器 (GRU-D-AE) 和高斯混合模型 (GMM) 进行集群.
主要成果:
- 确定了至少9个患者群,其中5个具有明显的临床相关性.
- 集群模式在多个训练中保持稳定.
- 观察到与传统策略中的表型重叠.
结论:
- 纵向EHR数据对于AOM前期的深度表型很有价值.
- 鉴定出具有临床意义的群体,对AOM选择有潜在影响.
- 需要进一步的研究来验证在更大的队列中发现的结果,并探索AOM反应相关性.
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