美国食品和药物管理局用自然语言处理叙述提高对药物安全报告重复数据删除的信心

Kory Kreimeyer, Oanh Dang, Jonathan Spiker, Paula Gish, Jessica Weintraub, E. Wu, R. Ball, T. Botsis
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引用次数: 3

摘要

美国食品和药物管理局(FDA)每年收到数以百万计的药物和治疗性生物制品上市后不良事件报告。这些提交的最突出的问题之一是报告重复,即一个患者经历的不良事件多次报告给FDA。重复对数据分析有重要的负面影响。我们改进和优化了现有的重复数据删除算法,该算法同时使用结构化和自由文本数据,开发了一个基于web的应用程序来支持数据处理,并进行了为期6个月的专门评估,以评估FDA重复数据删除过程的潜在可操作性。将算法预测与审稿人对27个病例系列审查文件(中位数为281份报告)的重复确定进行比较,平均双侧召回率和精度分别为0.71 (SD±0.32)和0.67 (SD±0.34)。总的来说,评论者对该算法很有信心,并表示有兴趣使用它。这些发现支持案例系列审查的重复数据删除过程的可操作性,作为人工审查的补充。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Increased Confidence in Deduplication of Drug Safety Reports with Natural Language Processing of Narratives at the US Food and Drug Administration
The US Food and Drug Administration (FDA) receives millions of postmarket adverse event reports for drug and therapeutic biologic products every year. One of the most salient issues with these submissions is report duplication, where an adverse event experienced by one patient is reported multiple times to the FDA. Duplication has important negative implications for data analysis. We improved and optimized an existing deduplication algorithm that used both structured and free-text data, developed a web-based application to support data processing, and conducted a 6-month dedicated evaluation to assess the potential operationalization of the deduplication process in the FDA. Comparing algorithm predictions with reviewer determinations of duplicates for twenty-seven files for case series reviews (with a median size of 281 reports), the average pairwise recall and precision were equal to 0.71 (SD ± 0.32) and 0.67 (SD ± 0.34). Overall, reviewers felt confident about the algorithm and expressed their interest in using it. These findings support the operationalization of the deduplication process for case series review as a supplement to human review.
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