EmbPred30：使用分类嵌入评估糖尿病患者30天再入院
EmbPred30: Assessing 30-days Readmission for Diabetic Patients using Categorical Embeddings
摘要：
医院再入院是一项重要的医疗质量指标，有助于确定医院为患者提供的护理质量水平，并且已被证明非常昂贵。据估计，在美国，由于糖尿病患者的再入院，每年花费超过250亿美元。本文对现有模型进行了基准测试，并提出了一种新的基于嵌入的最先进的深度神经网络（DNN）。根据1999-2008年间从130家美国医院收集的数据，该模型可以识别住院糖尿病患者是否会在30天内再次入院，准确率为95.2%，受试者操作特征下面积（AUROC）为97.4%。结果是令人鼓舞的病人有变化的药物，同时承认有很高的机会重新入院。确定潜在的病人再入院可以帮助医院系统改善他们的住院护理，从而节省他们不必要的开支。
英文摘要：
Hospital readmission is a crucial healthcare quality measure that helps in determining the level of quality of care that a hospital offers to a patient and has proven to be immensely expensive. It is estimated that more than $25 billion are spent yearly due to readmission of diabetic patients in the USA. This paper benchmarks existing models and proposes a new embedding based state-of-the-art deep neural network(DNN). The model can identify whether a hospitalized diabetic patient will be readmitted within 30 days or not with an accuracy of 95.2% and Area Under the Receiver Operating Characteristics(AUROC) of 97.4% on data collected from 130 US hospitals between 1999-2008. The results are encouraging with patients having changes in medication while admitted having a high chance of getting readmitted. Identifying prospective patients for readmission could help the hospital systems in improving their inpatient care, thereby saving them from unnecessary expenditures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
