对大型观测健康数据的线性模型进行惩罚方法比较
Egill A Fridgeirsson1, Ross Williams1, Peter Rijnbeek1
1Department of Medical Informatics, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands.
概括
L1和ElasticNet规范化方法为医疗预测提供了更好的区别. 基于L0的方法,如代硬值 (Iterative Hard Thresholding, IHT) 和破碎适应 (Broken Adaptive Ridge, BAR) 提供了更简单,更易于解释的模型,并具有更好的校准.
科学领域:
- 医疗保健中的机器学习
- 统计建模 统计建模
- 预测分析是一种预测分析.
背景情况:
- 后勤回归被广泛用于医疗预测.
- 规范化技术对于优化模型性能和防止过度装配至关重要.
- 评估各种规范化方法对于选择最有效的方法至关重要.
研究的目的:
- 为了比较不同后勤回归规范化变体的区分和校准性能.
- 评估这些方法在医疗预测模型中的内部和外部验证.
- 引导规范化技术的选择,以提高预测准确性和可解释性.
主要方法:
- 利用5个美国索赔和电子健康记录数据库对主要抑郁症患者群体的数据.
- 使用L1,L2,ElasticNet,自适应L1,自适应ElasticNet,破碎的自适应 (BAR) 和代硬值 (IHT) 开发和外部验证的后勤回归模型.
- 采用75%/25%的火车测试分割,并使用歧视 (AUC) 和校准指标评估性能,并通过弗里德曼测试和关键差异图进行统计分析.
主要成果:
- L1和ElasticNet的规范化表现出优越的内部和外部歧视性能.
- BAR和IHT方法表现出最好的内部校准,尽管没有任何一种方法导致外部校准.
- 虽然IHT和BAR稍微不那么有歧视性,但与L1和ElasticNet相比,它们显著降低了模型复杂性和特征数量.
结论:
- L1和ElasticNet为医疗保健中的物流回归提供了最佳的区分性能,确保了内部和外部验证的稳定性.
- 基于L0的方法 (IHT,BAR) 有利于创建更简单,更易于解释的模型,并增强节和校准.
- 这些发现有助于为医疗预测模型选择适当的规范化技术,平衡预测性能,模型复杂性和可解释性.
相关概念视频
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
37
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...
37
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
50
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
50
01:25Statistical Methods for Analyzing Epidemiological Data
361
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
361
01:20Comparing the Survival Analysis of Two or More Groups
177
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...
177
01:11Residuals and Least-Squares Property
7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K