A comparison of models for predicting early hospital readmissions
A comparison of models for predicting early hospital readmissions
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DOI：
10.1016/j.jbi.2015.05.016
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发表时间：
2015-08-01
影响因子：
4.5
通讯作者：
Lucas, Joseph
中科院分区：
文献类型：
--
作者：
Futoma, Joseph;Morris, Jonathan;Lucas, Joseph
医院和付款人之间的风险分担安排以及医疗保险和医疗补助中心 (CMS) 施加的处罚正在推动人们对减少早期再入院的兴趣。有许多已发表的风险模型可以预测特定患者群体的 30 天再入院率，但它们通常表现出较差的预测性能，并且不适合在临床环境中使用。在这项工作中，我们描述并比较了几种预测模型，其中一些模型从未应用于此任务，并且优于医疗保健文献中通常应用的回归方法。此外，我们将深度学习的方法应用于 CMS 用来惩罚医院的五种条件，并提供一个简单的框架来确定哪些条件最具成本效益。 (C) 2015 年作者。由爱思唯尔公司出版
Risk sharing arrangements between hospitals and payers together with penalties imposed by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early readmissions. There are a number of published risk models predicting 30 day readmissions for particular patient populations, however they often exhibit poor predictive performance and would be unsuitable for use in a clinical setting. In this work we describe and compare several predictive models, some of which have never been applied to this task and which outperform the regression methods that are typically applied in the healthcare literature. In addition, we apply methods from deep learning to the five conditions CMS is using to penalize hospitals, and offer a simple framework for determining which conditions are most cost effective to target. (C) 2015 The Authors. Published by Elsevier Inc.