通过预测医疗保健中错过的预约来提高健康公平:机器学习研究
Yi Yang1, Samaneh Madanian1, David Parry2
1Auckland University of Technology, Auckland, New Zealand.
JMIR medical informatics
|January 12, 2024
概括
机器学习模型准确地预测了错过医院预约的患者. 这可以通过确定有针对性的干预措施的风险人群来提高医疗保健效率和患者的结果.
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:
- 患者没有出现 (没有出现/出席/没有出席) 浪费医疗保健资源,并对患者的健康产生负面影响.
- 有效地预测患者没有出现的情况对于优化门诊预约安排和资源分配至关重要.
研究的目的:
- 开发和评估机器学习模型,以预测患者错过医院门诊预约的可能性.
- 评估后勤回归,随机森林和极端梯度增强 (XGBoost) 模型的性能.
主要方法:
- 利用中部地区卫生局 (MDHB) 的5年门诊病例记录 (1,080,566次访问).
- 开发并比较了三个机器学习模型:逻辑回归,随机森林和XGBoost.
- 采用10倍交叉验证和超参数调整,用于模型优化和评估,使用精度,灵敏度,特异性和AUROC.
主要成果:
- XGBoost模型表现出卓越的性能,AUC为0.92,灵敏度为0.83,特异性为0.85.
- 没有出现的关键预测因素包括患者的DNS历史,年龄,种族和预约时间.
- 在广泛的数据集上训练的机器学习模型可以有效地预测患者没有出现.
结论:
- 这项研究提出了机器学习在新西兰医疗保健系统中用于"没有显示" (DNS) 管理的新应用.
- 开发的模型作为概念验证,用于在MDHB和其他卫生委员会内对DNS预测进行基准测试.
- 建议进行进一步的定性研究,以了解DNS的根本原因,并制定针对性干预措施,以改善资源利用和健康公平.
关键词:
没有参加.没有显示显示.任命 没有遵守 没有遵守数据分析数据分析.决策支持系统 决策支持系统医疗保健操作 医疗保健操作卫生公平性健康公平性机器学习是机器学习.没有出现的患者.预测 预测 预测 预测预测建模预测建模更多相关视频
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