电子健康记录的隐藏空间表示,用于集群透析相关的功能衰竭亚型
Djeane Debora Onthoni1, Ming-Yen Lin2, Kuei-Yuan Lan1
1Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan.
Computers in biology and medicine
|October 6, 2024
概括
这项研究开发了一个框架,使用英国生物银行数据对功能衰竭亚型进行分类. 该模型确定了不同的组,将严重的慢性病 (CKD) 与较轻的形式和急性损伤 (AKI) 区分开来,有助于向治疗.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
背景情况:
- 功能衰竭呈现各种形式,如急性损伤 (AKI) 和慢性病 (CKD).
- 重叠的并发症和类似的治疗方法 (例如透析) 复杂化了亚型的分化.
- 准确的分类对于有效的患者管理和治疗策略至关重要.
研究的目的:
- 设计和验证一个端到端的框架,以集群功能衰竭的亚型.
- 利用英国生物库数据,专注于透析患者,以进行可靠的亚型识别.
- 为了提高对个性化医学的功能衰竭异质性的理解.
主要方法:
- 将英国生物库电子健康记录 (EHR) 数据转换为标准化矩阵,包括访问时间间隔.
- 使用卷积自编码器 (ConvAE) 进行隐性空间转换.
- 应用主要组件分析 (PCA) 和K-意味着对潜伏空间进行聚类,以识别亚型.
主要成果:
- 该框架成功地减少了数据的维度,并加速了计算.
- 两个不同的群体出现了:一个CKD多数群 (集群1) 存活率低,一个混合群 (集群0) 存活率高,可能表明较轻的CKD和AKI.
- 该模型有效地区分了功能衰竭的亚型,显示了改善临床分层的潜力.
结论:
- 开发的框架提供了一种新的方法来理解功能衰竭的异质性.
- 综合多种数据源提供了一个整体的观点,对患者护理至关重要.
- 这种方法通过准确地分类功能衰竭的亚型,促进了细微的治疗干预.
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