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使用可解释机器学习为临床部门构建绩效评分动态预测系统

Huashu Wen¹, Xiaohua Li¹, Haibo Zhang¹

¹General Hospital of Southern Theater Command of PLA, Guangzhou, 510010 Guangdong China.

Health information science and systems

|December 5, 2025

概括

本研究引入了一个动态框架,使用可解释的机器学习来预测公共医院部门的绩效. 该系统为早期干预和改善管理提供了及时的见解,提高了医疗保健的运营效率.

科学领域:

医疗保健管理的管理
机器学习在医学中的应用
公共卫生信息学公共卫生信息学

背景情况:

准确评估临床部门的绩效对于公共医院管理至关重要.
现有的方法依赖于静态的,追溯的数据,限制了早期干预和知情决策.
需要为实时绩效评估提供动态,可解释的框架.

研究的目的:

开发一个动态框架来预测年度部门绩效得分.
利用可解释的机器学习来提高性能指标的可解释性.
为支持公共医院管理决策提供实用工具.

主要方法:

从27个临床部门收集了24个月的绩效数据.
训练了六个机器学习模型 (LR,DT,RF,梯度提升,XGBoost,CatBoost) 来预测年度性能得分.
应用了SHapley添加式解释 (SHAP) 来实现模型解释性和确定关键绩效指标.

主要成果:

线性回归 (LR) 模型表现出高R2值和低误差指标 (RMSE,MAE,MSE) 的优异性能.
在所有时间窗口中,SHAP分析确定了一致的前五大绩效指标,包括收入比例和平均住院费用.
一个基于Web的预测工具ClinDeptPredictor是使用表现最好的LR模型开发的.

结论:

该研究提出了一种新的动态框架,用于使用可解释的机器学习进行临床部门性能预测.
该框架允许逐步监测绩效,并为管理决策提供及时的见解.
该ClinDeptPredictor工具促进了实际应用,并为医疗保健绩效管理提供了方法支持.

关键词:

人工智能的人工智能是人工智能.临床部门临床部门可解释的机器学习性能数据性能数据是指性能数据.预测模型的预测模型.网络系统网络系统

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