Feature selection for predictive analytics continues to be a major challenge in the healthcare industry, particularly as it relates to readmission prediction. Several research works in mining healthcare data have focused on structured data for readmission prediction. Even within those works that are based on unstructured data, significant gaps exist in addressing class imbalance, context specific noise removal which thus necessitates new approaches readmission prediction using unstructured data. In this work, a novel approach is proposed for feature selection and domain related stop words removal from unstructured with class imbalance in discharge summary notes. The proposed predictive model uses these features along with other relevant structured data. Five iterations of predictions were performed to tune and improve the models, results of which are presented and analyzed in this paper. The authors suggest future directions in implementing the proposed approach in hospitals or clinics aimed at leveraging structured and unstructured discharge summary notes.

预测分析的特征选择仍然是医疗保健行业的主要挑战，特别是与再入院预测有关。挖掘医疗保健数据的一些研究工作都集中在结构化数据上，用于再入院预测。即使在那些基于非结构化数据的工作中，在解决类不平衡，特定于上下文的噪声消除方面也存在巨大的差距，因此有必要使用新的方法来使用非结构化数据进行再入院预测。在这项工作中，提出了一种新颖的方法，用于从放电摘要笔记中的类不平衡的非结构化特征选择和领域相关停用词中删除。拟议的预测模型将这些功能与其他相关结构化数据一起使用。对预测进行了五次迭代以调整和改进模型，本文介绍并分析了结果。作者提出了在医院或诊所实施拟议方法的未来方向，旨在利用结构化和非结构化出院总结记录。