Historical Claims Data based Hybrid Predictive Models for Hospitalization
Chengcheng Liu, Yong Shi · Procedia Computer Science · 2014
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.