人工智能启用软件原型,从电子健康记录中告知阿片类药物监管:开发和可用性研究
Alfred Sorbello1, Syed Arefinul Haque1, Rashedul Hasan1
1Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.
JMIR AI
|September 29, 2023
概括
这项研究开发了一种AI原型,用于在电子健康记录 (EHR) 中检测药物不良事件 (ADEs),改进阿片类药物安全信号检测. 该工具有效地从非结构化的EHR数据中提取有价值的信息,减少了手工研究工作.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 药物监督 药物监督 药物监督
背景情况:
- 电子健康记录 (EHR) 包含了对药物安全性有价值的患者数据.
- 人工智能 (AI) 和自然语言处理 (NLP) 可以从非结构化的EHR文本中提取见解.
- 检测美国食品和药物管理局 (FDA) 监管的药物的安全信号可以通过EHR数据来增强.
研究的目的:
- 开发一个支持人工智能的软件原型,用于识别不良药物事件 (ADE) 安全信号.
- 从EHR中的自由文本放电摘要中提取ADE信号.
- 为了提高阿片类药物安全性,并支持FDA的研究活动.
主要方法:
- 开发了一个基于Web的软件原型,使用关键字/触发短语搜索,基于规则的算法和深度学习.
- 使用MedSpacy进行部分识别,以及Spark NLP for Healthcare用于在退院摘要中识别命名实体.
- 从MIMIC III数据库中提取了阿片类药物的候选ADEs,并从15名FDA工作人员那里收集了反.
主要成果:
- 从EHR文本中成功识别出已知与阿片类药物相关的不良药物反应.
- 实现的AI模型性能指标:准确性 (0.66),回忆力 (0.69),精度 (0.64),和F1得分 (0.67).
- 美国食品和药物管理局的参与者发现,该原型对于可用性,可视化和潜在的支持药物安全信号检测,节省时间和人工精力而言非常理想.
结论:
- 这种新型原型使用人工智能从非结构化的EHR文本中自动提取和分析临床有用信息.
- 它提高了使用现实世界的数据来监测阿片类药物安全性的效率.
- 该工具增加了监管审查的数据可用性,并减少了手动研究负担.
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