在OMOP CDM中进行的一项比较性患者级预测研究:合成数据的应用潜力和见解
Najia Ahmadi1, Quang Vu Nguyen2, Martin Sedlmayr2
1Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307, Dresden, Germany. najia.ahmadi@tu-dresden.de.
Scientific reports
|January 27, 2024
概括
本研究将基于Web的OHDSI工具 (ATLAS,PLP) 与OMOP CDM中机器学习患者级预测的原生R解决方案进行比较. PLP提供更快的执行和更容易的实施,但与本地R包相比,它具有特定的ML分类器的局限性.
科学领域:
- * 生物医学信息学
- * 医疗保健中的机器学习
- * 数据科学数据科学
背景情况:
- * 大规模的临床数据协作标准化和结合区域数据库,为临床预测模型创造有价值的资源.
- *机器学习 (ML) 对于处理广泛的患者数据来进行疾病分类和风险评估至关重要.
- * 观察健康数据科学和信息学 (OHDSI) 合作开发了一个框架,使用OMOP常用数据模型 (CDM) 用于ML应用.
研究的目的:
- * 为了比较OHDSI基于Web的工具 (ATLAS,患者级预测 (PLP)) 与OMOP CDM中基于ML的患者级预测的原生R解决方案的可行性.
- * 评估PLP R-Package在临床预测任务中的优缺点.
- *指导用户选择最合适的方法来进行基于ML的患者级预测分析.
主要方法:
- * 基准分析将OHDSI PLP R-Package与原生R溶液 (mlr3) 相比较.
- *对两种方法的性能,执行时间和模型实施的方便性进行评估.
- *将每个ML解决方案应用于OMOPCDM中相同的患者级预测任务.
主要成果:
- * PLP 软件包的执行时间更短,表明了良好的可扩展性和具有可视化功能的直观实现.
- * 与原生R包相比,在PLP中实施特定的ML分类器 (例如,Lasso) 存在局限性.
- *由于这些局限性,由于这些局限性,在现实世界的预测问题中发现了性能下降的可能性.
结论:
- *PLP R-Package在OMOP CDM中为患者级预测提供了速度和易用性的优势.
- *原生R包可能为特定的ML分类器提供优越的性能,影响现实世界的预测准确性.
- * 发现有助于选择合适的工具,用于大规模开发基于ML的临床预测模型.
相关概念视频
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43
01:20Comparing the Survival Analysis of Two or More Groups
188
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...
188
01:09Censoring Survival Data
95
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
95
01:24Kaplan-Meier Approach
141
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,...
141