Historical Claims Data Based Hybrid Predictive Models for Hospitalization

被引:1
|
作者
Liu, Chengcheng [1 ]
Shi, Yong [2 ]
机构
[1] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
[2] Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing, Peoples R China
关键词
D O I
10.1016/j.procs.2014.05.164
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Over $30 billion are wasted on unnecessary hospitalization each year, therefore it is needed to find a better quantitative way to identify patients who are mostly likely to be hospitalized and then provide them utmost care. As a good starting point, the objective of this paper was to develop a predictive model to predict how many days patients may spend in the hospital next year based on patients' historical claims dataset, which is provided by the Heritage Health Prize Competition. The proposed predictive model applied the ensemble of binary classification and regression techniques. The model is evaluated on testing dataset in terms of the Root-Mean-Square-Error (RMSE). The best RMSE score was 0.474, and the corresponding prediction accuracy 81.9% was reasonably high. Therefore it is convincing to conclude that predictive models have the potentials to predict hospitalization and improve patients' quality of life.
引用
收藏
页码:1791 / 1800
页数:10
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