Measuring performance in health care: case-mix adjustment by boosted decision trees
Measuring performance in health care: case-mix adjustment by boosted decision trees
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DOI：
10.1016/j.artmed.2004.06.001
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发表时间：
2004-10-01
影响因子：
7.5
通讯作者：
Lepage, E
中科院分区：
文献类型：
--
作者：
Neumann, A;Holstein, J;Lepage, E
目的：本文的目的是调查提升决策树的适用性的情况下，组合调整参与比较各种医疗保健entities.Methods的性能：首先，我们提出了逻辑回归，决策树，并提升决策树在一个统一的框架。其次，我们详细研究了它们的应用程序的两个常见的性能指标，重症监护的死亡率和潜在的可避免的再入院率。结果：对于这两个例子的技术，提高决策树优于标准的预后模型，特别是线性逻辑回归模型，预测能力。另一方面，提升决策树计算要求很高，产生的模型是相当复杂的，需要额外的工具interpretation.Conclusion：提升决策树是一个强大的工具，在医疗保健性能测量的情况下组合调整。根据每个上下文中设置的特定优先级，预测能力的增益可能会补偿使用提升决策树的不便。(C)2004 Elsevier B.V.保留所有权利。
Objective: The purpose of this paper is to investigate the suitability of boosted decision trees for the case-mix adjustment involved in comparing the performance of various health care entities.Methods: First, we present logistic regression, decision trees, and boosted decision trees in a unified framework. Second, we study in detail their application for two common performance indicators, the mortality rate in intensive care and the rate of potentially avoidable hospital readmissions.Results: For both examples the technique of boosting decision trees outperformed standard prognostic models, in particular linear logistic regression models, with regard to predictive power. On the other hand, boosting decision trees was computationally demanding and the resulting models were rather complex and needed additional tools for interpretation.Conclusion: Boosting decision trees represents a powerful tool for case-mix adjustment in health care performance measurement. Depending on the specific priorities set in each context, the gain in predictive power might compensate for the inconvenience in the use of boosted decision trees. (C) 2004 Elsevier B.V. All rights reserved.