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利用数据管道和LLM来推进患者安全事件研究

Fagun Shah¹, Yue Yu², Yuheng Shi²

¹University of Texas at Dallas, Richardson, Texas, USA.

Studies in health technology and informatics

|May 17, 2025

概括

本研究引入了ETL-LLM管道,以标准化医疗器械报告 (MDR) 数据分析,提高事件分类准确性和效率,以改善患者安全研究.

科学领域:

医疗器械安全医疗器械安全
医疗信息学医疗信息学
自然语言处理自然语言处理.

背景情况:

从MAUDE数据库中提取医疗器械报告 (MDR) 数据往往缺乏明确的方法,阻碍了可重复性.
不一致的数据处理影响了患者安全研究的可靠性.

研究的目的:

开发和演示一个提取-转换-负载 (ETL) 管道与大型语言模型 (LLM) 结合起来,用于分析MDR叙述.
提高医疗器械数据中报告的不良事件分类的准确性和效率.
探索这种方法在患者安全研究中的应用.

主要方法:

利用OpenFDA API和自定义的MAUDE ETL管道来标准化MDR数据提取和转换.
采用大型语言模型 (LLM) 来分析处理的MDR中的自由文本叙述.
展示了使用MDRs专门用于内镜粘膜切除装置的ETL-LLM方法.

主要成果:

在ETL-LLM管道标准化MDR数据,使得更一致的分析.
自由文本叙述的LLM驱动分析提高了事件分类的准确性和效率.
该方法显示了在分析各种医疗器械数据方面更广泛应用的潜力.

结论:

开发的ETL-LLM管道为分析复杂的MDR数据提供了一个强大的方法.
标准化数据处理和LLM分析对于推进数据驱动的患者安全研究至关重要.
建议进一步扩大MDR样本大小和多样性,以增强研究能力.

关键词:

莫德 (Maude) 是一个女士.数据管道数据管道大型语言模型患者安全患者安全

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