基于机器学习的预测,无法显示远程医疗的遭遇
C Mahony Reategui-Rivera1, Wanting Cui1, Stefan Escobar-Agreda2
1Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, USA.
Telemedicine reports
|July 9, 2025
概括
机器学习模型适度预测秘鲁的远程医疗没有出现. 具有成本敏感性的增强技术可以改善高风险患者的识别,从而改善医疗保健的获取.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 远程医疗 运营研究 科研
背景情况:
- 患者不出现对医疗保健系统构成重大挑战,导致资源浪费和减少对护理的准入.
- 远程医疗已经扩大了医疗保健的准入,但约会遵守仍然是一个关键的操作因素.
- 预测和减轻没有出现的情况对于优化远程医疗服务的提供至关重要,特别是在秘鲁等多样化的医疗保健环境中.
研究的目的:
- 评估各种机器学习 (ML) 模型对秘鲁远程医疗预约中患者不出现的预测性能.
- 确定与远程医疗预约缺席相关的主要人口,社会经济和系统层面的因素.
- 为了比较不同的ML技术的有效性,包括集体方法和类失衡策略,在预测没有出现.
主要方法:
- 一项回顾性观察性研究分析了2019年6月至2023年11月的150多万个远程医疗预约.
- 多个ML模型 (随机森林,XGBoost,LightGBM,异常检测) 使用低样本和成本敏感学习实现.
- 模型性能被严格评估,使用精度,回忆,特异性,AUC,F1得分和10次代的准确性等指标.
主要成果:
- 具有成本敏感性的XGBoost表现出平衡的性能,AUC为0.722和准确度为0.799,有效识别高风险患者.
- 具有成本敏感性的随机森林实现了最高的特异性 (0.843) 和精度 (0.832),尽管回忆率较低.
- 使用XGBoost进行低采样,回忆值为0.654,这表明它有很强的检测不显示实例的能力.
结论:
- 机器学习模型为秘鲁医疗系统内的远程医疗不出现提供了适度的预测能力.
- 具有成本敏感性的增强技术有效地提高了高风险缺席的患者的识别.
- 干预措施应量身定制,考虑个人行为和系统层面的因素,以改善预约遵守和医疗保健公平.
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