开发多服务机器学习模型,以预测病例发布时的术后停留时间和出院处置
Hamed Zaribafzadeh1, T Clark Howell1, Wendy L Webster2
1From the Department of Surgery, Duke University, Durham, NC.
概括
机器学习模型只使用初始病例发布数据,准确地预测术后停留时间和出院处置. 这有助于优化医院资源分配和选择性住院手术的出院计划.
科学领域:
- 机器学习在医疗保健分析中的应用.
- 医院操作的预测建模.
- 手术结果和资源管理
背景情况:
- 目前的外科手术安排优先考虑手术室的可用性,而不是下游资源需求.
- 预测术后停留时间和出院情况对于高效的医院管理至关重要.
- 早期预测有助于更好的资源配置和排放规划.
研究的目的:
- 开发机器学习模型来预测术后停留时间 (LOS) 和出院处置 (DD).
- 仅使用手术病例发布时可用的数据来进行这些预测.
- 将这些模型应用于成人选择性住院病例的多个外科服务.
主要方法:
- 对63574名接受选择性住院手术的成年患者进行了回顾性研究.
- 发展渐变增强决策树分类模型.
- 使用案例发布数据预测LOS (短期,中期,长期) 和DD (家庭与非家庭).
主要成果:
- 洛斯模型实现了接收器运行特征曲线 (AUC) 下的面积为0.81.
- DD模型显示,家庭与非家庭预测的AUC为0.88.
- 纳入相对值单位和历史LOS提高了短期和长期LOS预测的准确性.
结论:
- 机器学习模型可以在病例发布时有效预测术后的LOS和DD.
- 这些模型支持改进病例安排,资源分配和病床利用.
- 预测模型有助于更早地规划出院,防止因床位不可用而导致病例取消.
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