使用LLMs,回归和基于树的模型预测儿科诊断成像患者不显示和延长等待时间
Daniel Rafique1,2, Xuan Liu2, Bo Gong3,4
1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Frontiers in artificial intelligence
|September 19, 2025
概括
机器学习模型可以预测儿科患者的不出现和放射学中的长时间等待. 语境预约数据提高了预测准确性,使得优化调度能够更好地获得医疗保健.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的机器学习
- 医疗保健服务研究 医疗服务研究
背景情况:
- 诊断和干预放射科的患者不出现和长时间等待导致资源效率低下和患者不满.
- 预测建模可以通过识别有风险的患者来帮助缓解这些问题.
- 优化预约安排对于改善医疗保健准入和运营效率至关重要.
研究的目的:
- 调查机器学习方法,以预测儿科患者没有出现和长时间等待 (超过1小时) 的放射学预约.
- 为这些任务确定最有效的预测模型和数据特征.
- 为调度团队提出针对性的下游住宿方案.
主要方法:
- 使用六种预测模型进行了实验:极端梯度增强 (XGBoost),随机森林 (RF),支持矢量机,物流回归,人工神经网络和大型语言模型 (LLM).
- 利用了20个功能,包括患者人口统计和预约相关数据.
- 采用数据平衡方法,如实例硬度值 (IHT) 和类权重.
- 在LLM中使用和不使用连续上下文数据进行性能比较.
主要成果:
- 在没有显示预测方面,XGBoost获得了最佳AUC (0.96) 和F1得分 (0.62).
- 随机森林为等待时间预测提供了最好的AUC (0.83) 和F1得分 (0.61).
- 在90%的概率值下,LLM表现强,并经过了强大的校准.
结论:
- 对于公平的预测,上下文预约数据比患者人口统计数据更重要.
- 开发的预测模型可以为基于风险的安排提供信息,以改善患者获得护理的机会.
- 进一步整合预测输出可以优化资源分配,减少预约延迟.
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