深度表示学习用于从电子健康记录中聚类纵向生存数据
Jiajun Qiu1, Yao Hu1, Li Li1
1Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riβ, Germany.
Nature communications
|March 15, 2025
概括
这项研究介绍了VaDeSC-EHR,这是一个新的机器学习工具,用于从电子健康记录中识别患者子组. 它通过揭示不同疾病轨迹和风险的不同患者群体来改善精准医学.
科学领域:
- 计算生物学是一种计算生物学.
- 生物医学信息学是生物医学信息学.
- 机器学习在医疗保健中的应用
背景情况:
- 精准医学需要识别不同的患者亚组,以进行量身定制的治疗.
- 电子健康记录 (EHR) 为使用机器学习发现这些子组提供了巨大的潜力.
- 现有的方法往往难以捕捉诊断轨迹和风险事件中的复杂相互作用,导致异构的子组.
研究的目的:
- 开发和评估VaDeSC-EHR,一个基于变压器的变化自编码器,用于从EHR中集群纵向生存数据.
- 为了解决捕获复杂患者数据相互作用的局限性,以改善子组识别.
- 通过更准确的患者分层,加强精准医学策略的开发.
主要方法:
- 实现VaDeSC-EHR,一种基于变压器的新型变异自动编码器架构.
- 从电子健康记录中提取的纵向生存数据的聚类.
- 使用已知集群标签的合成和现实世界的基准数据集进行验证.
主要成果:
- 与基线方法相比,VaDeSC-EHR在基准数据集上的表现优越.
- 该模型成功地在克罗恩病患者中确定了四个不同的亚组.
- 这些子组表现出不同的诊断轨迹和风险概况,突出了临床和遗传相关因素.
结论:
- VaDeSC-EHR有效地识别出具有明显临床和分子特征的患者子组.
- 该方法改进了分析复杂EHR数据的现有方法.
- VaDeSC-EHR通过使新型患者子组发现成为推进精准医学的强大工具.
相关概念视频
01:18Introduction To Survival Analysis
150
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
150
01:21Cancer Survival Analysis
315
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
315
01:20Actuarial Approach
50
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
50
01:24Kaplan-Meier Approach
74
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
74
01:20Comparing the Survival Analysis of Two or More Groups
113
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
113
01:15Assumptions of Survival Analysis
81
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
81