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基于两阶段自适应阈值集成学习算法的门诊患者及时取药预测模型研究
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| 篇名： | 基于两阶段自适应阈值集成学习算法的门诊患者及时取药预测模型研究 |
| TITLE： | Research on the timely medication retrieval prediction model for outpatients based on a two-stage adaptive threshold ensemble learning algorithm |
| 摘要： | 目的 构建门诊患者及时取药预测模型，精准识别延迟取药的高风险患者，为智慧药房差异化报到策略的制定与资源优化配置提供数据支撑。方法基于西安交通大学第一附属医院2025年1－3月的680568条门诊有效处方数据，先通过K均值聚类算法（K-means）与高斯混合模型（GMM）进行双聚类分析，结合轮廓系数择优确定取药时间差自适应阈值，以此划分“及时取药”与“延迟取药”，构建二元目标变量；通过多方法融合的策略筛选六大类特征；从区分度、整体性能与校准度3个维度对6种基学习器和4种集成学习模型进行性能评估，并开展模型解释性分析。结果双聚类分析结果显示，GMM的轮廓系数优于K-means（0.7024vs.0.6988），最终确定的自适应阈值为49.82min。纳入处方中，有74.99%的处方为及时取药，25.01%为延迟取药。10个候选模型中，堆叠集成（Stacking）模型表现最优，测试集曲线下面积为0.9544、F1分数为0.9424、准确率为0.9115、Brier分数为0.066，区分度与校准度俱佳。模型解释性分析结果显示，模型的预测受患者历史行为、诊断相关特征等多因素共同驱动。结论本研究构建了基于两阶段自适应阈值集成学习算法的门诊患者及时取药预测模型，其精准度与稳定性较高，可实现对患者取药行为的动态判定。 |
| ABSTRACT： | OBJECTIVE To construct a predictive model for timely medication retrieval of outpatients， accurately identify high-risk patients with delayed medication retrieval， and provide data support for the development of differentiated registration strategies and resource optimization allocation in smart pharmacies. METHODS Based on 680 568 valid outpatient prescription records from January to March 2025 at the First Affiliated Hospital of Xi’an Jiaotong University， a dual-clustering analysis was conducted using K-means algorithm and Gaussian mixture model （GMM）. An adaptive threshold for medication retrieval time difference was determined by combining contour coefficients， and “timely medication retrieval” and “delayed medication retrieval” were divided to construct binary objective variables； six types of features were screened through a multi-method fusion strategy； the performance of 6 kinds of base learners and 4 kinds of ensemble learning models were evaluated from three dimensions： discrimination， overall performance， and calibration， and explanatory analysis of the models were conducted. RESULTS The results of the dual-clustering analysis showed that the silhouette coefficient of GMM was better than K-means （0.702 4 vs. 0.698 8）， and the final adaptive threshold was determined to be 49.82 min. Among the prescriptions included， 74.99% were for timely medication retrieval and 25.01% were for delayed medication retrieval. Among the 10 candidate models， the Stacking model performed the best， with an area under the test set curve of 0.954 4， F1 score of 0.942 4， accuracy of 0.911 5， Brier score of 0.066， and good discrimination and calibration. The explanatory analysis results of the model showed that its predictions were driven by multiple factors such as patient historical behavior， and diagnostic related characteristics. CONCLUSION This study constructed a timely medication retrieval prediction model for outpatients based on a two-stage adaptive threshold ensemble learning algorithm， which has high accuracy and stability， and can achieve dynamic judgment of patient medication retrieval behavior. |
| 期刊： | 2025年第36卷第24期 |
| 作者： | 范园园；王丰；曾攀科；封卫毅 |
| AUTHORS： | FAN Yuanyuan，WANG Feng，ZENG Panke，FENG Weiyi |
| 关键字： | 门诊处方；及时取药预测；聚类分析；机器学习；集成学习 |
| KEYWORDS： | outpatient prescription； timely medication retrieval prediction； cluster analysis； machine learning； ensemble |
| 阅读数： | 6 次 |
| 本月下载数： | 0 次 |
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