Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c.
Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c.
复制标题
混合效应机器学习：预测血红蛋白A1c纵向变化的框架。
DOI：
10.1016/j.jbi.2018.09.001
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发表时间：
2019-01
影响因子：
4.5
通讯作者：
McCoy RG
中科院分区：
文献类型：
--
作者：
Ngufor C;Van Houten H;Caffo BS;Shah ND;McCoy RG
随着时间的推移，准确可靠的临床进展预测有可能改善慢性疾病的预后。分析纵向数据的经典方法是使用（广义）线性混合效应模型（GLMM）。然而，线性参数模型是基于假设的，往往难以验证。相比之下，数据驱动的机器学习方法可以应用于从原始数据中获得洞察力，而无需先验假设。然而，大多数机器学习算法的基本理论假设数据是独立的和同分布的，这使得它们对于纵向监督学习效率低下。在这项研究中，我们制定了一个分析框架，将GLMM的随机效应结构集成到非线性机器学习模型中，该模型能够利用纵向数据中固有的时间异质性效应、稀疏和变长患者特征。我们应用衍生的混合效应机器学习（MEml）框架来预测控制良好的成人2型糖尿病患者血红蛋白A1c （HbA1c）测量的血糖控制的纵向变化。结果表明，MEml与传统的GLMM具有竞争力，但在性能上大大优于不考虑随机效应的标准机器学习模型。其中，MEml预测晚期患者第1、2、3、4次就诊时血糖变化的准确率分别是梯度增强模型的1.04、1.08、1.11、1.14倍，其他方法结果相似。为了进一步证明MEml的普遍适用性，我们使用真实的公开数据集和合成数据集进行了一系列实验，以提高准确性和鲁棒性。这些实验强化了MEml相对于其他方法的优越性。总的来说，本研究的结果强调了基于纵向数据的机器学习方法中随机效应建模的重要性。我们的MEml方法对相关数据具有很强的抵抗力，可以很容易地解释随机效应，并以高精度预测现实世界临床环境中纵向临床结果的变化。
Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assumptions, which are often difficult to verify. In contrast, data-driven machine learning methods can be applied to derive insight from the raw data without a priori assumptions. However, the underlying theory of most machine learning algorithms assume that the data is independent and identically distributed, making them inefficient for longitudinal supervised learning. In this study, we formulate an analytic framework, which integrates the random-effects structure of GLMM into non-linear machine learning models capable of exploiting temporal heterogeneous effects, sparse and varying-length patient characteristics inherent in longitudinal data. We applied the derived mixed-effect machine learning (MEml) framework to predict longitudinal change in glycemic control measured by hemoglobin A1c (HbA1c) among well controlled adults with type 2 diabetes. Results show that MEml is competitive with traditional GLMM, but substantially outperformed standard machine learning models that do not account for random-effects. Specifically, the accuracy of MEml in predicting glycemic change at the 1st, 2nd, 3rd, and 4th clinical visits in advanced was 1.04, 1.08, 1.11, and 1.14 times that of the gradient boosted model respectively, with similar results for the other methods. To further demonstrate the general applicability of MEml, a series of experiments were performed using real publicly available and synthetic data sets for accuracy and robustness. These experiments reinforced the superiority of MEml over the other methods. Overall, results from this study highlight the importance of modeling random-effects in machine learning approaches based on longitudinal data. Our MEml method is highly resistant to correlated data, readily accounts for random-effects, and predicts change of a longitudinal clinical outcome in real-world clinical settings with high accuracy.
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影响因子：
3
作者：
McCoy RG;Ngufor C;Van Houten HK;Caffo B;Shah ND
通讯作者：
Shah ND
影响因子：
4.8
作者：
De'Ath, G
通讯作者：
De'Ath, G
影响因子：
7.2
作者：
DEYO, RA;CHERKIN, DC;CIOL, MA
通讯作者：
CIOL, MA
DOI：
10.1198/106186006x133933
发表时间：
2006-09-01
影响因子：
2.4
作者：
Hothorn, Torsten;Hornik, Kurt;Zeileis, Achim
通讯作者：
Zeileis, Achim
影响因子：
5.8
作者：
Stekhoven, Daniel J.;Buehlmann, Peter
通讯作者：
Buehlmann, Peter