何银平, 谭先姣, 王萍. 基于XGboost模型的视网膜疾病患者住院费用探讨. 2026. biomedRxiv.202604.00168
基于XGboost模型的视网膜疾病患者住院费用探讨
通讯作者: 王萍, wpwy88@163.com
DOI:10.12201/bmr.202604.00168
Exploration of hospitalization expenses for retinal disease patients based on XGboost model
Corresponding author: Wang ping, wpwy88@163.com
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摘要:目的 探讨视网膜疾病患者住院费用差异及其影响因素,为医院成本控制提供数据支撑。方法 收集2021年1月1日至2024年12月31日的视网膜疾病患者相关数据,采用Mann-Whitney U检验比较不同医师组在不同分层下的住院费用差异,并通过Cliff’s delta效应量量化差异的实际意义。运用XGBoost回归模型结合SHAP值进行住院费用预测及影响因素分析。结果 不同医师组在不同分层下的住院费用差异具有统计学意义(P<0.05),效应量较小(δ<0.25)。XGboost预测性能较好,训练集MAPE为7.11%,R2为0.96,验证集MAPE为18.64%,R2为0.80。DRG支付标准和药品费对整体预测影响最大,是影响住院费用的关键因素。结论 本研究通过可解释的机器学习模型,揭示了影响住院费用的关键因素,能为优化医疗资源配置、规范诊疗行为和制定合理的成本控制策略提供了科学依据。
Abstract: Objective To explore the differences in hospitalization costs of patients with retinal diseases and the influencing factors of hospitalization expenses, in order to provide data support for hospital cost control. Method The data of patients with retinal diseases from January 1, 2021 to December 31, 2024 were collected. The Mann-Whitney U test was employed to compare the differences in hospitalization costs among various physician groups across different stratifications. Additionally, an XGBoost regression model, combined with SHAP values, was utilized to predict hospitalization costs and to analyze the factors influencing them. Results The differences in hospitalization costs among different physician groups under different stratification were statistically significant (P<0.05), and the effect size is small (δ<0.25). The impact of various factors on hospitalization costs: high-impact factors such as drug costs and DRG payment standards, medium-impact factors including laboratory fees, examination fees, age, and physician group, and low-impact factors such as fee types, consumables fees, and surgical fees. XGBoost exhibits strong predictive performance, with a training set MAPE of 7.11% and an R2 of 0.96, and a validation set MAPE of 18.64% and an R2 of 0.80. Conclusion This study reveals the key factors influencing hospitalization costs using interpretable machine learning models, which can offer a scientific basis for optimizing the allocation of medical resources, regulating diagnostic and treatment behaviors, and formulating cost control strategies that are reasonable.
Key words: Hospitalization expenses; Attending physician groups; XGboost model; Retinal diseases; Influencing factors1提交时间:2026-04-28
版权声明:作者本人独立拥有该论文的版权,预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。 -
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序号 提交日期 编号 操作 1 2026-03-30 10.12201/bmr.202604.00168V1
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