自然语言处理用于自动分类和优先级的个案安全报告的自动分类和优先级的个案评估
Thomas Lieber1, Helen R Gosselt1, Pelle C Kools2
1Netherlands Pharmacovigilance Centre Lareb, 's-Hertogenbosch, Netherlands.
这项研究通过使用自然语言处理 (NLP) 来分析自由文本字段来增强安全报告的分类. 改进的预测模型有效地优先考虑个人病例安全报告 (ICSR) 进行临床审查.
科学领域:
- 药物监督 药物监督 药物监督
- 计算语言学计算语言学
- 机器学习 机器学习
背景情况:
- 个体病例安全报告 (ICSR) 需要有效的分类,以便及时进行临床审查.
- 现有的预测模型可以通过从自由文本字段中整合非结构化数据来改进.
研究的目的:
- 通过使用自然语言处理 (NLP) 来整合来自自由文本的特性来增强ICSR分类的预测模型.
- 改善需要临床评估的ICSR优先级.
主要方法:
- 一个包装分类器模型被训练使用结构化和NLP衍生的特征.
- 通过一个词袋模型,从自由文本字段中提取了NLP特征,删除了停止词,选择了有意义的词.
- 专家识别的信号词和数字转换的分类变量 (证据权重) 也被纳入.
主要成果:
- 开发的包装分类器实现了曲线下的面积 (AUC) 0.921 (95% CI = 0.918-0.925).
- 影响分类的关键特征包括仿制药名称,文本长度,解剖治疗化学 (ATC) 代码,体重指数 (BMI) 和患者年龄.
结论:
- 通过NLP增强的预测模型在识别需要临床审查的ICSR方面表现强.
- 该模型可以帮助药监督评估人员优先考虑ICSR,提高安全信号检测的效率.
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