A two-stage super learner for healthcare expenditures.
A two-stage super learner for healthcare expenditures.
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DOI：
10.1007/s10742-022-00275-x
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发表时间：
2022-12
影响因子：
1.5
通讯作者：
中科院分区：
文献类型：
--
作者：
通过引入一种非常适合数据呈现强偏斜和零通货膨胀情况的新方法来改进医疗保健支出的估计。模拟和两个真实世界的数据集：2016-2017年医疗支出小组调查（MEPS）;使用纵向数据的背痛结局（BOLD）。超级学习器是一种集成机器学习方法，它可以联合收割机来改进估计。我们提出了一个两阶段的超级学习者，它非常适合医疗支出数据，通过分别估计任何医疗支出的概率和医疗支出的平均金额条件下有医疗支出。然后，这些估计值可以合并，以产生每个观察的单一支出估计值。分析策略可以灵活地为每个估计阶段结合一系列单独的估计方法，包括基于回归的方法和机器学习算法，如随机森林。我们比较了两阶段超级学习者与一阶段超级学习者的性能，以及在模拟和真实的数据中的广泛数据设置下估计医疗成本的多个个体算法。使用均方误差和R2比较预测性能。我们的研究结果表明，两阶段的超级学习者有更好的性能相比，一个阶段的超级学习者和个人算法，在各种各样的设置下，在模拟和实证分析的医疗成本估计。两阶段超级学习者比一阶段超级学习者的改进在零通货膨胀高的情况下尤其明显。
To improve the estimation of healthcare expenditures by introducing a novel method that is well-suited to situations where data exhibit strong skewness and zero-inflation. Simulations, and two real-world datasets: the 2016–2017 Medical Expenditure Panel Survey (MEPS); the Back Pain Outcomes using Longitudinal Data (BOLD). Super learner is an ensemble machine learning approach that can combine several algorithms to improve estimation. We propose a two-stage super learner that is well suited for healthcare expenditure data by separately estimating the probability of any healthcare expenditure and the mean amount of healthcare expenditure conditional on having healthcare expenditures. These estimates can then be combined to yield a single estimate of expenditures for each observation. The analytical strategy can flexibly incorporate a range of individual estimation approaches for each stage of estimation, including both regression-based approaches and machine learning algorithms such as random forests. We compare the performance of the two-stage super learner with a one-stage super learner, and with multiple individual algorithms for estimation of healthcare cost under a broad range of data settings in simulated and real data. The predictive performance was compared using Mean Squared Error and R2. Our results indicate that the two-stage super learner has better performance compared with a one-stage super learner and individual algorithms, for healthcare cost estimation under a wide variety of settings in simulations and in empirical analyses. The improvement of the two-stage super learner over the one-stage super learner was particularly evident in settings when zero-inflation is high.
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影响因子：
9.7
作者：
Berk, ML;Monheit, AC
通讯作者：
Monheit, AC
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9.7
作者：
Finkelstein, Eric A.;Trogdon, Justin G.;Dietz, William
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影响因子：
3.4
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通讯作者：
Zaslavsky AM
影响因子：
3
作者：
Cohen, Joel W.;Cohen, Steven B.;Banthin, Jessica S.
通讯作者：
Banthin, Jessica S.