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使用机器学习预测眼科诊所缺勤率:使用全国数据开发和验证模型

Finley Breeze¹, Ruhella R Hossain^1,2, Michael Mayo³

¹Department of Ophthalmology, University of Auckland, Auckland, New Zealand.

Clinical & experimental ophthalmology

|October 27, 2023

概括

机器学习使用基本数据准确预测眼科诊所的缺勤情况,为当前的策略提供了具有成本效益的替代方案. 这可以改善医疗保健的获取,并减少新西兰的不平等.

科学领域:

眼科医生眼科眼科
医疗信息学医疗信息学
机器学习机器学习

背景情况:

在新西兰,眼科诊所的缺勤导致健康状况不佳,不平等,并带来巨大的成本.
目前改善出席率的策略昂贵且无效.

研究的目的:

开发和验证机器学习模型,准确预测眼科诊所缺勤情况.

主要方法:

一项回顾性观察性研究分析了来自新西兰公共眼科诊所的310万次预约 (2009-2018).
在XGBoost和物流回归模型的训练和优化中,使用重复的十倍交叉验证对人口和临床相关变量进行了反复的十倍交叉验证.
在区域数据子集上训练的模型与全国模型进行了比较.

主要成果:

在区域数据上训练的XGBoost模型实现了最高的预测性能 (平均AUROC 0.764).
XGBoost的性能优于逻辑回归 (平均AUROC为0.756,p=0.002).
具体区域模型的表现优于单个全国性的模型 (平均AUROC 0.754,p=0.04).

结论:

机器学习模型可以有效地利用现有数据预测眼科诊所的缺勤情况.
需要进一步研究如何将这些算法应用于调度和公共卫生干预.

关键词:

流行病学流行病学股权资本股权资本机器学习是机器学习.

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