使用混合机器学习和基于规则的方法对与低标准药物相关的质量缺陷问题的自动分类
Desmond Chun Hwee Teo1, Yiting Huang2, Sreemanee Raaj Dorajoo2
1Vigilance and Compliance Branch, Health Products Regulation Group, Health Sciences Authority, 11 Biopolis Way #11-01 Helios, Singapore, 138667, Singapore. desmond_teo@hsa.gov.sg.
Drug safety
|September 30, 2023
概括
一个机器学习模型有效地分类了不符合标准的医学问题,提高了公共卫生安全. 这种方法有助于监管机构优先考虑关键的产品缺陷,以更快地降低风险.
科学领域:
- 药物监督 药物监督 药物监督
- 监管科学 监管科学
- 医疗保健中的人工智能
背景情况:
- 低质量的药物对公众健康构成异质的风险.
- 卫生产品监管机构需要有效的方法来优先考虑关键质量缺陷.
- 及时降低风险对公共安全至关重要.
研究的目的:
- 为了分类不合格药物的问题的性质.
- 增加基于风险的及时审查产品质量缺陷案件.
- 改进药物的市场后监督工作.
主要方法:
- 开发了一种结合机器学习和基于关键字的模型 (CISTERM).
- 从产品缺陷案例中使用文本数据来分类质量问题.
- 监管活动-健康科学局 (MedDRA-HSA) 最低级别的术语用于分类.
主要成果:
- 该模型实现了对关键缺陷类别的高回忆度和精度.
- 对于"产品被改/被禁止物质"的回忆 (精度):92% (96%).
- 对于"不符合规范/趋势"的回忆 (精度): 86% (90%).
- 对于"制造不合规"的回忆 (精度):90% (91%).
结论:
- 机器学习可以提高产品质量缺陷审查的优先级.
- 一个综合模型有助于及时识别不合格药物的问题.
- 这种方法支持药物监管机构对药物进行有效的市场后监管.
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