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气象因素驱动的布地奈德高用药日预测集成学习模型的构建与比较
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| 篇名： | 气象因素驱动的布地奈德高用药日预测集成学习模型的构建与比较 |
| TITLE： | Meteorological factor-driven prediction of high-use days of budesonide: construction and comparison of ensemble learning models |
| 摘要： | 目的 通过气象因素构建预测布地奈德高用药日的集成学习模型，为医院药房管理提供参考。方法基于三明市中西医结合医院主管区域2024年气象数据及同期该院的布地奈德门诊使用数据，将布地奈德门诊使用数据的第75百分位数定义为高用药日，并相应形成高用药日数据集，将预测问题转化为分类问题，构建随机森林模型、极端梯度提升模型、直方图梯度提升分类模型3种集成学习模型。以准确率、精确率、召回率、F1分数、对数损失函数为指标评估模型的性能，通过沙普利可加性特征解释（SHAP）方法分析模型的可解释性。结果直方图梯度提升分类模型的性能最佳（准确率＝0.75，F1分数＝0.48），其次为极端梯度提升模型（准确率＝0.74，F1分数＝0.43）和随机森林模型（准确率＝0.72，F1分数＝0.22）；SHAP结果提示后2个模型的预测结果相关性最高。结论集成学习模型可有效预测布地奈德的高用药日，其中直方图梯度提升分类模型的预测能力最优；低温、高湿和低气压对布地奈德日用药量预测存在显著正向影响。 |
| ABSTRACT： | OBJECTIVE To construct ensemble learning models for predicting high-use days of budesonide based on meteorological factors， thereby providing reference for hospital pharmacy management. METHODS Meteorological data for 2024 and outpatient budesonide usage data from the jurisdiction of Sanming Hospital of Integrated Traditional Chinese and Western Medicine were collected. High-use days were defined as the 75th percentile of outpatient budesonide usage， and a corresponding dataset was established. The prediction task was formulated as a classification problem， and three ensemble learning models were developed： Random Forest， Extreme Gradient Boosting （XGBoost）， and Histogram-based Gradient Boosting Classifier. Model performance was evaluated using accuracy， precision， recall， F1-score， and log-loss. Model interpretability was analyzed using Shapley Additive Explanations （SHAP）. RESULTS The Histogram-based Gradient Boosting Classifier achieved the best performance （accuracy＝0.75， F1-score＝0.48）， followed by XGBoost （accuracy＝0.74， F1-score＝0.43） and Random Forest （accuracy＝0.72， F1-score＝0.22）. SHAP results suggested that the prediction results of the last two models have the highest correction. CONCLUSIONS Ensemble learning models can effectively predict high-use days of budesonide， with the Histogram- based Gradient Boosting Classifier demonstrating the best predictive performance. Low temperature， high humidity， and low atmospheric pressure show significant positive impacts on the prediction of daily budesonide usage. |
| 期刊： | 2025年第36卷第21期 |
| 作者： | 陈祺焘；周悦；张晓俊；倪璟雯；孙国强；高分飞；夏丽珍；李梓豪 |
| AUTHORS： | CHEN Qitao，ZHOU Yue，ZHANG Xiaojun，NI Jingwen，SUN Guoqiang，GAO Fenfei，XIA Lizhen，LI Zihao |
| 关键字： | 布地奈德；气象因素；集成学习；可解释性人工智能 |
| KEYWORDS： | budesonide； meteorological factors； ensemble |
| 阅读数： | 90 次 |
| 本月下载数： | 1 次 |
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