使用分类树和回归树对损伤成本的多因素分析
Roumen Vesselinov1, Kartik Kaushik1, Mark Scarboro1
1National Study Center for Trauma and Emergency Medical Systems, University of Maryland at Baltimore, Baltimore, Maryland.
这项研究表明,医疗专业人员费用占伤害成本的很大一部分,综合因素可以比单个指标更好地预测费用. 下肢和腹部的伤害是最昂贵的.
科学领域:
- 卫生经济学 卫生经济学
- 创伤护理 创伤护理
- 医疗数据分析 医学数据分析
背景情况:
- 大多数研究都低估了损伤总成本,因为它们只关注医院费用.
- 专业费用,包括住院后的护理,占整体医疗费用的重要组成部分.
- 准确的成本结构对于有效的医疗保健资源分配和政策制定至关重要.
研究的目的:
- 通过分析全部医疗费用,包括医院费用和专业费用,确定受伤结构的占主导地位的成本.
- 开发使用机器学习的分类和回归树 (CART) 预测伤害成本的多因素模型.
- 为更高层次的数据集创建一个可推断的成本结构模型.
主要方法:
- 利用马里兰州全州医院数据 (2017-2022) 和创伤登记数据 (2016-2021).
- 分析了医院费用 (床,护理,药品) 和专业费用 (医生,康复,治疗).
- 员工分类和回归树 (CART) 构建多因素成本模型,结合AIS和ISS等伤害严重程度指标.
主要成果:
- 医院费用平均占总伤害成本的71%,而专业费用占29%.
- 开发了四种基于CART的伤害成本模型:有/没有伤害组,全成本和仅专业费用.
- 确定了特定的成本损害组,并验证了多因素模型的预测能力.
结论:
- 多创伤患者的伤害成本最高,其次是孤立和轻微的伤害.
- 最好的成本预测涉及因素的组合,而不是单一的变量.
- 下肢和腹部受伤非常昂贵;头部受伤可能会产生长期成本影响. 专业费用约占损伤总成本的三分之一.
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