在MAUDE中揭示内镜手术:一种ETL-LLM方法
Yuheng Shi1, Eric Yang1, Yang Gong1
1University of Texas Health Science Center at Houston, Houston, Texas, USA.
Studies in health technology and informatics
|July 1, 2025
概括
从MAUDE数据库中提取患者安全数据的标准化至关重要. 一种ETL-LLM方法增强了医疗器械事件报告的分析,改善了临床见解和患者安全研究.
科学领域:
- 医疗信息学 医疗信息学
- 患者安全研究 患者安全研究
- 健康 数据科学 数据科学
背景情况:
- 马德数据库对于患者安全研究至关重要,但数据提取方法不一致,阻碍了分析.
- 对事件报告分析缺乏标准化的方法,导致临床医生无法获得可靠的见解.
研究的目的:
- 开发和验证一种标准化的方法,从MAUDE数据库中提取和分析医疗器械事件报告.
- 提高患者安全数据分析的一致性和可重复性.
主要方法:
- 一个ETL (提取,转换,加载) 管道与大型语言模型 (LLM) 结合起来.
- 该方法应用于内镜剪辑报告,用于提取和分析分类和叙事数据.
- 专注于标准化不良事件报告的识别和解释.
主要成果:
- 该ETL-LLM方法有效地提取和分析了分类和叙事报告.
- 确定了与患者并发症,手术程序和设备使用有关的关键见解.
- 证明了数据标准化和可解释性的改进.
结论:
- 拟议的ETL-LLM方法提供了一种透明和有效的方法来分析MAUDE数据.
- 这种标准化有可能迅速通知临床医生,并推进患者安全研究.
- 鼓励利用开放访问数据库进行进一步的研究,以提高患者安全.
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