用知识图表增强的深度学习用于制药需求预测
Xiaofang Chen1,2, Gang Lu1, Hao Zhang3
1School of Management, Wuhan University of Technology, Wuhan, 430070, China.
Scientific reports
|January 6, 2026
概括
准确的制药需求预测得到了改进,这是一种新的KG-GCN-LSTM模型. 这种以知识图表增强的深度学习方法捕捉了复杂的药物需求模式,优于更好的医疗保健供应链的现有方法.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 供应链管理 供应链管理
背景情况:
- 药品需求预测对于医疗保健供应链效率至关重要,但受到复杂,动态的需求模式的挑战.
- 现有的统计和机器学习模型很难从药物替代和季节性趋势等因素中捕捉非线性.
研究的目的:
- 开发一种新的混合模型,将制药知识图与深度学习相结合,以改善需求预测.
- 在复杂的医疗保健环境中提高制药需求预测的准确性和稳定性.
主要方法:
- 拟议的KG-GCN-LSTM:一种混合模型,将制药知识图 (KG) 与图形卷积网络 (GCN) 和长短期记忆 (LSTM) 网络相结合.
- 利用GCN从KG的历史药物需求和相关药物中提取特征.
- 采用LSTM来捕捉药物需求模式中的时间动态.
主要成果:
- KG-GCN-LSTM在已建立的基准标准上表现出卓越的表现,包括ARIMA,SVR,XGBoost,RNN,CNN-LSTM,TimeMixer和NBEATS.
- 与NBEATS相比,对称平均绝对百分比误差 (SMAPE) 降低了3.62%.
- 性能与最先进的TimeMixer模型相当.
结论:
- 通过知识图表增强的深度学习,特别是KG-GCN-LSTM,显著提高了制药需求预测的准确性和稳定性.
- 该模型有效地捕捉了复杂的非线性需求模式,为数据驱动的医疗保健供应链管理提供了宝贵的支持.
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