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Application of Data Mining Techniques to Predict the Length of Stay of Hospitalized Patients with Diabetes

机译：数据挖掘技术在预测住院糖尿病患者的住院时间长度

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Diabetes is one of the most critical public health conditions worldwide. It has been shown that patients with diabetes are associated with a longer length of hospital stay (LOS) and increased associated healthcare cost. The uncertainty of diabetic patients' LOS makes it difficult for hospitals to optimize their scheduling process. In this paper, we applied the stacked ensemble method, with deep learning as the meta-learning algorithm, to predict long vs. short LOS for diabetic patients. The obtained results show that stacked ensemble technique is promising in this field because stacking multiple classification learning algorithms resulted in a better predictive performance than that obtained from any of the constituent learning algorithms. Having a reasonable estimate on LOS for patients with diabetes can help in optimizing the use of hospital resources, reducing healthcare cost, and improving diabetic patient satisfaction.
机译：糖尿病是全球最关键的公共卫生条件之一。已经表明，糖尿病患者与较长长度的医院住宿（LOS）相关，并增加了相关的医疗费用。糖尿病患者洛杉矶的不确定性使医院难以优化其调度过程。在本文中，我们应用了堆叠的集合方法，深入学习作为元学习算法，预测糖尿病患者的长VS。所获得的结果表明，堆叠的集合技术在该领域具有前景，因为堆叠多种分类学习算法导致比从任何组成学习算法所获得的更好的预测性能。对糖尿病患者的LOS有合理的估算可以帮助优化医院资源的使用，降低医疗费用，提高糖尿病患者满意度。

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