Risk prediction for heterogeneous populations with application to hospital admission prediction.
Risk prediction for heterogeneous populations with application to hospital admission prediction.
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DOI：
10.1111/biom.12769
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发表时间：
2018-06
期刊：
影响因子：
1.9
通讯作者：
Smith M
中科院分区：
文献类型：
--
作者：
Huling JD;Yu M;Liang M;Smith M
本文的动机是日益需要对为多样化和复杂的患者提供服务的大型医院和医疗保健系统进行风险建模。通常，人群的异质性是由慢性病等一系列因素决定的。当这些分层因素导致子群体重叠时，重叠群体的协变量效应可能具有一定的相似性。我们通过对预测入院等结果的变量的重要性施加结构性限制来利用这种相似性。我们的基本假设是，如果一个变量对于患有其中一种慢性病的亚群很重要，那么它对于患有这两种慢性病的亚群也应该很重要。然而，一个变量对于患有两种特定慢性病的亚群可能很重要，但对于仅患有这两种病之一的亚群则不那么重要。这一假设及其对更多条件的推广是合理的，并且极大地有助于在亚人群中借用力量。我们证明了我们的估计方法的预言属性，并表明即使结构假设被错误指定，我们的方法仍然会包含大样本中所有真正的非零变量。我们在广泛的数值研究以及大型医疗保健提供者的医疗保险人群的入院预测和验证中的应用展示了我们的方法的令人印象深刻的性能。
This article is motivated by the increasing need to model risk for large hospital and health care systems that provide services to diverse and complex patients. Often, heterogeneity across a population is determined by a set of factors such as chronic conditions. When these stratifying factors result in overlapping subpopulations, it is likely that the covariate effects for the overlapping groups have some similarity. We exploit this similarity by imposing structural constraints on the importance of variables in predicting outcomes such as hospital admission. Our basic assumption is that if a variable is important for a subpopulation with one of the chronic conditions, then it should be important for the subpopulation with both conditions. However, a variable can be important for the subpopulation with two particular chronic conditions but not for the subpopulations of people with just one of those two conditions. This assumption and its generalization to more conditions are reasonable and aid greatly in borrowing strength across the subpopulations. We prove an oracle property for our estimation method and show that even when the structural assumptions are misspecified, our method will still include all of the truly nonzero variables in large samples. We demonstrate impressive performance of our method in extensive numerical studies and on an application in hospital admission prediction and validation for the Medicare population of a large health care provider.
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DOI：
10.1198/016214506000000735
发表时间：
2006-12-01
影响因子：
3.7
作者：
Zou, Hui
通讯作者：
Zou, Hui
影响因子：
4.5
作者：
Zhao, Peng;Rocha, Guilherme;Yu, Bin
通讯作者：
Yu, Bin
DOI：
10.1198/016214501753382273
发表时间：
2001-12-01
影响因子：
3.7
作者：
Fan, JQ;Li, RZ
通讯作者：
Li, RZ
影响因子：
1.1
作者：
Percival, Daniel
通讯作者：
Percival, Daniel
DOI：
10.1111/j.2517-6161.1996.tb02080.x
发表时间：
1996-01-01
影响因子：
5.8
作者：
Tibshirani, R
通讯作者：
Tibshirani, R