用于从产科事故调查报告中提取种族不平等相关洞见的智能多文档摘要
Intelligent Multi-Document Summarisation for Extracting Insights on Racial Inequalities from Maternity Incident Investigation Reports
AI总结：
本研究提出I-SIRch:CS框架，整合SIRch分类概念标注、聚类与生成式摘要技术，用于产科安全事故报告的分析汇总，在188份报告数据集上验证显示BART模型摘要效果最优。
摘要：
在医疗领域，每年会发生数千起安全事故，但从这些事故中汲取的经验教训并未得到有效汇总。利用人工智能分析事故报告，可通过识别反复出现的模式和影响因素，挖掘出关键洞见以预防伤害。为汇总和提取有价值的信息，可运用自然语言处理（NLP）和机器学习技术对非结构化数据进行摘要和挖掘，从而可能揭示系统性问题和需要改进的优先领域。本文提出I-SIRch:CS框架，该框架旨在推动安全事故报告的汇总与分析，同时确保整个过程的可追溯性。该框架将基于安全智能研究（SIRch）分类体系的概念标注与聚类、摘要及分析功能相整合。研究采用包含188份匿名产科调查报告的数据集，这些报告标注了27个SIRch人为因素概念；I-SIRch:CS利用句子嵌入和k-means聚类将带标注的句子分组为聚类簇，并通过文件和句子ID保持可追溯性。研究使用离线的最先进生成式摘要模型（BART、DistilBART、T5）为每个聚类簇生成摘要，并通过评估摘要质量属性的指标对这些模型进行评估和比较。生成的摘要会关联回原始文件和句子ID，确保可追溯性并支持对摘要信息的验证。结果表明，BART在生成信息丰富且简洁的摘要方面具有优势。
英文摘要：
In healthcare, thousands of safety incidents occur every year, but learning from these incidents is not effectively aggregated. Analysing incident reports using AI could uncover critical insights to prevent harm by identifying recurring patterns and contributing factors. To aggregate and extract valuable information, natural language processing (NLP) and machine learning techniques can be employed to summarise and mine unstructured data, potentially surfacing systemic issues and priority areas for improvement. This paper presents I-SIRch:CS, a framework designed to facilitate the aggregation and analysis of safety incident reports while ensuring traceability throughout the process. The framework integrates concept annotation using the Safety Intelligence Research (SIRch) taxonomy with clustering, summarisation, and analysis capabilities. Utilising a dataset of 188 anonymised maternity investigation reports annotated with 27 SIRch human factors concepts, I-SIRch:CS groups the annotated sentences into clusters using sentence embeddings and k-means clustering, maintaining traceability via file and sentence IDs. Summaries are generated for each cluster using offline state-of-the-art abstractive summarisation models (BART, DistilBART, T5), which are evaluated and compared using metrics assessing summary quality attributes. The generated summaries are linked back to the original file and sentence IDs, ensuring traceability and allowing for verification of the summarised information. Results demonstrate BART's strengths in creating informative and concise summaries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
