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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >An improved support vector machine-based diabetic readmission prediction
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An improved support vector machine-based diabetic readmission prediction

机译：一种改进的支持向量机基础机基糖尿病阅读预测

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摘要

Background and objective: In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early identification of unplanned readmission risks will improve the quality of care during hospitalization and reduce the occurrence of readmission. In clinical practice, medical workers generally use LACE score method to evaluate patient readmission risks, but this method usually performs poorly. With this in mind, this study presents a novel method combining support vector machine and genetic algorithm to build the risk prediction model, which simultaneously involves feature selection and the processing of imbalanced data. This model aims to provide decision support for clinicians during the discharge management of patients with diabetes.
机译：背景和目标：在医疗保健系统中，无计划的入院的成本占总医院支付的大部分。 特定于医院的入院率成为世界各地的关键问题。 量化和早期识别无计划的入院风险将改善住院期间的护理质量，减少入院的发生。 在临床实践中，医疗工作者通常使用蕾丝评分方法来评估患者的入院风险，但这种方法通常表现不佳。 考虑到这一点，本研究提出了一种组合支持向量机和遗传算法来构建风险预测模型的新方法，其同时涉及特征选择和不平衡数据的处理。 该型号旨在为糖尿病患者放电管理提供临床医生的决策支持。

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