在临床预测模型的电子健康记录数据中解决缺失的方法:比较评估
Jean Digitale1,2, Deborah Franzon3, Mark J Pletcher2
1National Clinician Scholars Program, University of California, San Francisco, San Francisco, CA, United States.
JMIR medical informatics
|November 14, 2025
概括
处理电子健康记录 (EHR) 中缺少的数据对于预测模型至关重要. 最后进行的观察 (LOCF) 和本地机器学习支持为临床预测中缺少的EHR数据提供了有效的解决方案.
科学领域:
- 临床信息学 临床信息学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 缺少的数据在基于电子健康记录 (EHR) 的预测建模中构成了重大挑战.
- 传统的归算方法可能不适合预测或机器学习模型,需要适应的工作流程,既开发和实时预测.
研究的目的:
- 评估在儿科重症监护室 (PICU) 中临床预测模型的电子健康记录中处理缺失数据的方法.
主要方法:
- 生成合成数据集,具有不同的缺失数据机制和比例,来自真实的电子健康记录数据.
- 评估的归算策略:最后进行的观察 (LOCF),随机森林多重归算,以及对缺失值的本地支持.
- 对预测成功的输出管 (二进制) 和血压 (连续) 的评估性能.
主要成果:
- 在886名患者和1220次输管事件中,缺少18.2%的原始EHR数据.
- LOCF表现出最小的归算误差,并且一般优于其他方法.
- 对二进制结果的推算方法性能比连续结果更有差异.
结论:
- 对于预测模型来说,传统的归算方法可能不是最优的.
- 缺失数据的比例显著影响了业绩,超过了缺失机制.
- 在预测分析中,LOCF和本机机器学习支持提供了高效和有效的解决方案来处理缺少的EHR数据.
相关概念视频
Mechanistic Models: Compartment Models in Individual and Population Analysis
235
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...
235
Methods of Documentation VII: EMR
1.4K
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
1.4K
Kaplan-Meier Approach
547
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,...
547
Strategies for Assessing and Addressing Confounding
345
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
345
Comparing the Survival Analysis of Two or More Groups
542
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...
542
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
472
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
472


