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使用机器学习算法预测未出席预约的决策分析框架

Carolina Deina¹, Flavio S Fogliatto², Giovani J C da Silveira³

¹Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° Andar, Porto Alegre, 90035-190, Brazil. caroldeina@gmail.com.

BMC health services research

|January 5, 2024

概括

这项研究引入了符号回归 (SR) 和实例硬度值 (IHT) 来预测患者不出现,优于现有方法. 这种新的方法确保了对医疗保健资源优化的强大模型概括.

科学领域:

医疗保健分析医疗保健分析
机器学习机器学习
数据科学数据科学数据科学

背景情况:

患者不出现会对医疗保健系统和患者的治疗结果产生负面影响.
机器学习提供了一个解决方案,可以预测没有出现的情况,从而实现主动的资源管理.

研究的目的:

开发和评估一个新的框架来预测患者没有出现,特别是解决不平衡的数据集.
引入和评估符号回归 (SR) 和实例硬度值 (IHT) 进行不显示预测.

主要方法:

提出了一个框架,包括一种新的双z折交叉验证.
符号回归 (SR) 和实例硬度值 (IHT) 与KNN,SVM,RUS,SMOTE和NearMiss-1进行了比较.
该框架在两个巴西医院的数据集上得到了验证,其不出现率各不相同.

主要成果:

符号回归 (SR) 和实例硬度值 (IHT) 在预测患者不出现方面表现出卓越的表现.
实例硬度值 (IHT) 在所有测试的分类算法中表现出色,性能变化低.
该研究取得了高灵敏度结果,在两个数据集上都超过了0.94,超过了现有的文献.

结论:

这项研究是首次将SR和IHT应用于患者无病预测,并实施双z交叉验证.
这些发现强调了由于在不平衡的数据集上运行不够的验证而导致偏差结果的风险.
拟议的框架增强了模型概括和稳定性分析,以准确地预测不显示的情况.

关键词:

分类算法的分类算法.医疗保健环境医疗环境一个不平衡的数据集.机器学习机器学习错过的预约错过的预约重新采样技术重新采样技术

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