Abstract：
The MAUDE database is a valuable public resource for understanding malfunctions and adverse events related to medical devices and health IT. However, its extensive data and complex structure pose challenges. To overcome this, we have developed an automated analytical pipeline using GPT-4, a cutting-edge large language model. This pipeline is intended to efficiently extract, categorize, and visualize safety events with minimal human annotation. In our analysis of 4,459 colonoscopy reports from MAUDE (2011-2021), the events were categorized into operational, human factor, and device-related. Ishikawa diagrams visualized a subset stored in a vector database for easy retrieval and comparison through a similarity search. This innovative approach streamlines access to vital safety insights, reducing the workload on human annotators, and holds promise to enhance the utility of the MAUDE database.
摘要：
MAUDE数据库是了解与医疗设备和健康IT相关的故障和不良事件的宝贵公共资源。然而,其广泛的数据和复杂的结构带来了挑战。为了克服这一点，我们使用先进的大型语言模型GPT-4开发了自动分析管道。这条管道旨在有效地提取，归类,并以最少的人为注释可视化安全事件。在我们对MAUDE(2011-2021)的4459例结肠镜检查报告的分析中，这些事件被归类为可操作的，人为因素,和设备相关。Ishikawa图可视化了存储在矢量数据库中的子集，以便通过相似性搜索进行轻松检索和比较。这种创新方法简化了对重要安全见解的访问，减少人类注释者的工作量，并有望增强MAUDE数据库的实用性。
