利用自然语言处理和机器学习方法在电子健康/医疗记录中检测不良药物事件:一个范围审查
Su Golder1, Dongfang Xu2, Karen O'Connor3
1Department of Health Sciences, University of York, York, YO10 5DD, UK. su.golder@york.ac.uk.
Drug safety
|January 9, 2025
概括
自然语言处理 (NLP) 和机器学习 (ML) 显示出在电子健康记录 (EHR) 中检测不良药物事件 (ADEs) 的前景. 虽然有效地识别了报告不足的事件,但标准化方面的挑战阻碍了药物监测的广泛采用.
科学领域:
- 药物监督和健康信息学
背景情况:
- 非结构化的电子健康记录 (EHR) 数据有可能用于检测药物不良事件 (ADEs).
- 自然语言处理 (NLP) 和机器学习 (ML) 是利用这些数据的关键技术.
- 对于NLP/ML在ADE检测中的实际有效性,需要进一步的证据.
研究的目的:
- 审查NLP/ML在从非结构化EHR数据中识别ADEs方面的有效性.
- 为了将NLP/ML与其他数据源进行比较,以改善药监督.
主要方法:
- 六个数据库的范围审查于2023年7月进行.
- 包括使用NLP/ML用于在EHR中识别ADE的研究.
- 进行了数据提取和叙事合成,以总结发现.
主要成果:
- 包括七项研究,分析了各种不良反应和药物.
- 包括深度学习在内的NLP/ML技术,改善了报告不足的ADE和安全信号的检测.
- 在方法和评估方面存在显著的变化,并指出了整合方面的挑战.
结论:
- NLP/ML为使用非结构化EHR数据的药监测提供了重大潜力.
- 这些方法擅长识别特定的ADE和新型安全信号.
- 缺乏标准化的方法和验证标准阻碍了广泛采用.
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