机器学习预测马来西亚主要三级医院门诊无人预约的情况
Abdullah Fahim Ahmad Hamdan1, Azuraliza Abu Bakar2
1Pathology Department, Hospital Kuala Lumpur, Ministry of Health Malaysia, Kuala Lumpur, Malaysia.
The Malaysian journal of medical sciences : MJMS
|November 6, 2023
概括
机器学习准确地预测了患者不出现的情况,梯度提升实现了78%的准确性. 这种模式可以帮助减少任命差距,改善医疗保健资源管理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 预测分析是一种预测分析.
背景情况:
- 没有患者的预约会破坏治疗的连续性和浪费资源.
- 机器学习提供了一种有前途的方法来预测和减轻没有出现的事件.
研究的目的:
- 在吉隆坡医院 (HKL) 开发患者不出现预约的预测模型.
- 应用机器学习算法来分析门诊预约数据.
主要方法:
- 利用来自HKL患者管理系统 (2019) 的246,943个门诊预约记录.
- 采用了七种机器学习算法:逻辑回归,决策树,k-NN,天真贝叶斯,随机森林,梯度增强和MLP.
- 对13个任命属性进行描述性和预测性分析.
主要成果:
- 描述性分析显示,28%的没有出现率受到预约月份和患者性别的影响.
- 梯度提升模型表现出最高的性能,准确度为78%,F1得分为0.76,AUC为0.65.
- 确定了影响没有出现的关键因素.
结论:
- 开发的预测模型可以告知有针对性的干预措施,以减少患者不出现.
- 实施该模型可以提高患者护理质量,并优化医疗机构的资源配置.
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