在初级保健中预测错过的预约:个性化的机器学习方法
Wen-Jan Tuan1, Yifang Yan2, Bilal Abou Al Ardat3
1Department of Family and Community Medicine, Penn State College of Medicine, Hershey, Pennsylvania wtuan@pennstatehealth.psu.edu.
Annals of family medicine
|July 28, 2025
概括
机器学习准确地预测了初级保健不出现和迟迟取消的情况. 时间表的领先时间是关键因素,使个性化策略能够改善约会遵守.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 初级保健管理管理初级保健管理
背景情况:
- 在初级保健中,错过预约会带来重大挑战.
- 预测和干预患者不出现和延迟取消是复杂的.
- 优化预约遵守对于医疗保健效率和患者结果至关重要.
研究的目的:
- 利用机器学习和大数据分析来预测错过的初级保健预约.
- 识别出不出现和延迟取消的关键因素.
- 开发个性化的策略来改善患者的坚持.
主要方法:
- 使用临床,社会经济和气候数据进行了回顾性纵向研究 (2019年1月至2023年6月).
- 开发多类机器学习模型 (梯度提升,随机森林,神经网络,物流回归).
- 功能重要性分析和按性别和种族/种族进行分层分析,以获得模型公平性.
主要成果:
- 分析包括109,328名患者和1,118,236名预约;6.9%的未出现,6.8%的延迟取消.
- 梯度增强模型表现出高性能 (AUC为0.852的不出现,0.921的延迟取消).
- 预约领先时间被确定为错过预约的最重要的预测因素;没有检测到偏差.
结论:
- 建立了一个强大的框架来预测错过的约会.
- 可以制定个性化策略,以提高患者对初级保健的坚持.
- 机器学习提供了一种实用的方法来应对错过预约的持续挑战.
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