基于证据的临床工程:健康信息技术不良事件的识别和分类与自然语言处理
Alessio Luschi1, Paolo Nesi1,2, Ernesto Iadanza3
1Department of Information Engineering, University of Florence, Via di S. Marta, 3, 50139, Firenze (FI), Italy.
Heliyon
|November 13, 2023
概括
本研究介绍了一种使用自然语言处理的AI框架,用于识别健康信息技术中的不良事件. 该系统通过分析来自数据库的真实世界的证据来支持医疗器械监管和安全监控.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 与健康信息技术 (HIT) 相关的不良事件正在增加.
- 现有的系统需要手动分析来识别这些事件.
- 医疗器械法规 (欧盟MDR 2017/745) 需要强有力的市场后监督.
研究的目的:
- 开发一个用于提取现实世界证据 (RWE) 的框架,以支持医疗技术评估,管理,维护和市场后监督.
- 利用自然语言处理 (NLP) 和人工智能 (AI) 来自动识别与HIT相关的不良事件.
- 提高医疗器械安全监控的效率和准确性.
主要方法:
- 关于自发报告系统,HIT故障分类和NLP的文献综述.
- 开发一个使用NLP技术和可解释AI模型的框架.
- 在FDA MAUDE数据库中的3075份手动标记的不良事件报告上微调和测试ClinicalBERT模型.
主要成果:
- 证明了使用NLP和AI自动识别不良事件报告的可行性.
- 在针对HIT不良事件的专用数据集上成功微调了ClinicalBERT.
- 建立了一个用于RWE提取的基础,以支持监管合规和设备管理.
结论:
- 拟议的框架有效地使用NLP和AI来识别医疗器械中的不良事件.
- 这种方法可以显著改善市场后监督和医疗技术管理.
- 该研究强调了人工智能在确保医疗器械安全和监管遵守方面日益重要.
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