预测患者不出现:用不完美的数据进行定位机器学习
Christopher Gyldenkærne1, Jakob Grue Simonsen2, Gustav From3
1Department of People and Technology, Roskilde University, Denmark.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究开发了机器学习模型来预测患者没有出现在门诊手术中,实现了高准确度. 医院人员的参与改善了模型的性能,证明了降低医疗保健成本的有希望的方法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 运营研究 运营研究
背景情况:
- 患者不出现代表了医疗保健机构的重大财务负担和运营挑战.
- 准确预测患者不出现的情况对于优化资源配置和改善患者流动至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测患者没有出现在门诊手术中.
- 评估定位工作和员工参与对模型性能的影响.
- 为了确定在内镜病房中预测不显示的最佳性能ML模型.
主要方法:
- 使用了最初未用于ML目的收集的患者历史数据.
- 员工在医院内工作,以了解数据实践和完善模型.
- 训练并比较各种ML模型,包括XGBoost与过量采样.
- 使用灵敏度,特异性和准确度指标评估模型性能.
主要成果:
- 性能最好的模型 (采用过量采样的XGBoost) 实现了0.97的灵敏度,0.66的特异性和0.95.95的准确性.
- 定位工作和员工参与导致模型性能有显著的定量改进.
- 开发的模型显示了对患者不出现的高预测能力.
结论:
- 机器学习模型,特别是过量采样的XGBoost,显示出在门诊外科手术环境中预测患者不出现的巨大潜力.
- 医院工作人员参与的协作设计对于提高ML模型的性能和适用性至关重要.
- 虽然有希望,但这些模型对其他医院病房和机构的通用性需要进一步验证和适应.
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