在小样本多个单位制造的药品质量预测和诊断的AI集成IQPD框架:从经验驱动到数据驱动的制造
Kaiyi Wang1,2, Xinhai Chen1,2, Nan Li1,2
1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China.
Acta pharmaceutica Sinica. B
|September 2, 2025
概括
这项研究引入了药品制造的智能质量预测和诊断 (IQPD) 框架. 新的PeDGAT模型提高了质量预测的准确性和稳定性,特别是在小样本系统中.
科学领域:
- 制药业 制造业
- 质量控制
- 数据科学
背景情况:
- 制药行业在质量数字化方面面临挑战,特别是复杂的多阶段流程和小样本系统.
- 传统的质量预测模型在有限的数据场景中难以实现单位间的依赖和稳定性.
研究的目的:
- 为药品质量数字化开发智能质量预测和诊断 (IQPD) 框架.
- 为提高准确性和稳定性提出一种全新的路径增强双组合质量预测模型 (PeDGAT).
- 增强制药行业的流程透明度和数据驱动制造.
主要方法:
- 开发了一种新的路径增强双组合质量预测模型 (PeDGAT),结合了图表注意力网络和路径信息.
- 采用了四年的数据, 来自Tong Ren Tang公司的四个生产单位.
- 在IQPD框架内使用灰色相关分析和专家知识整合了诊断模型.
- 在人类-网络-物理系统中实现了IQPD框架.
主要成果:
- PeDGAT模型取得了最先进的结果,在预测准确度上平均提高了13.18%,稳定性提高了87.67%.
- 诊断模型减少了对大样本的依赖,提供了对属性关系的全面视图,并提高了过程透明度.
- 对于一个销的药品,IQPD框架促进了更快的决策和实时的质量调整.
结论:
- 开发的IQPD框架和PeDGAT模型显著提高了制药制造业的质量预测和诊断能力,特别是在小样本系统中.
- 该框架使得从经验驱动到数据驱动的制造业的转变成为可能,提高了整体工艺效率和透明度.
- 这种方法为复杂的制药生产环境中数字化质量控制提供了可扩展的解决方案.
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