使用机器学习模型优化急性尾炎患者的诊断相关组
Xinlong Gu1, Niannian Li2, Heng Wang3,4
1Teaching Management Department, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Frontiers in public health
|September 18, 2025
概括
急性尾炎 (AA) 的住院费用受住院时间,婚姻状况,手术和医院水平的影响. 一个决策树模型有助于开发与诊断相关的组 (DRG) 以偿还成本.
科学领域:
- 卫生经济学 卫生经济学
- 医疗信息学医学信息学
- 医疗保健管理的管理
背景情况:
- 诊断相关群体预期支付系统 (DRG-PPS) 是一个全球标准.
- 对医疗保健系统来说,定位DRG的分组和定价至关重要.
- 急性尾炎 (AA) 的成本因素需要进行局部分析.
研究的目的:
- 确定影响AA患者住院费用的关键因素.
- 根据本地数据,开发一个DRG对AA的分组模型.
- 为个人医院住院费用报销策略提供信息.
主要方法:
- 在中国合肥市 (2017-2019) 的医院进行分层随机抽样.
- 使用单因子和多重线性回归分析了4066个AA患者记录.
- 应用分类和回归树 (CART) 模型与E-CHAID用于DRG分组.
主要成果:
- 住院时间,婚姻状况,手术和住院水平显著影响了AA住院费用 (p<0.05).
- 一个CART模型将AA住院患者分为10个DRG组,使用年龄,手术类型和LOS.
- 标准化疾病成本从3047元人民币到15569元人民币不等.
结论:
- AA 住院费用明显与临床和人口因素有关.
- 开发的决策树模型为DRG分类提供了一个框架.
- 这些发现支持实施精确的,个性化的退款政策.
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