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Swiss Agency for Therapeutic Products, Swissmedic, Hallerstrasse 7, 3012, Bern, Switzerland.
BMC Med Inform Decis Mak. 2022 Dec 22;22(1):338. doi: 10.1186/s12911-022-02085-0.
Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, in order to find and extract relevant articles from the pack. This task is critical, as serious Adverse Drug Reactions (ADRs) still account for a large number of hospital admissions each year.
The aim of this study is to develop an augmented intelligence methodology for automatically identifying relevant publications mentioning an established link between a Drug and a Serious Adverse Event, according to the European Medicines Agency (EMA) definition of seriousness.
The proposed pipeline, called LiSA (for Literature Search Application), is based on three independent deep learning models supporting a precise detection of safety signals in the biomedical literature. By combining a Bidirectional Encoder Representations from Transformers (BERT) algorithms and a modular architecture, the pipeline achieves a precision of 0.81 and a recall of 0.89 at sentences level in articles extracted from PubMed (either abstract or full-text). We also measured that by using LiSA, a medical reviewer increases by a factor of 2.5 the number of relevant documents it can collect and evaluate compared to a simple keyword search. In the interest of re-usability, emphasis was placed on building a modular pipeline allowing the insertion of other NLP modules to enrich the results provided by the system, and extend it to other use cases. In addition, a lightweight visualization tool was developed to analyze and monitor safety signal results.
Overall, the generic pipeline and the visualization tool proposed in this article allows for efficient and accurate monitoring of serious adverse drug reactions from the literature and can easily be adapted to similar pharmacovigilance use cases. To facilitate reproducibility and benefit other research studies, we also shared a first benchmark dataset for Serious Adverse Drug Events detection.
在药物警戒学中，检测科学文献中归因于药物的安全信号是一个基本问题。出版物数量的不断增加需要自动化完成这项繁琐的任务，以便从文献中找到并提取相关文章。这项任务至关重要，因为每年仍有大量严重的药物不良反应（ADR）导致患者住院。
本研究旨在开发一种增强型人工智能方法，根据欧洲药品管理局（EMA）对严重性的定义，自动识别提及药物与严重不良事件之间既定关联的相关出版物。
该方法称为 LiSA（文献搜索应用程序），基于三个独立的深度学习模型，为生物医学文献中的安全信号检测提供支持。通过结合双向编码器表示的转换器（BERT）算法和模块化架构，该管道在从 PubMed 提取的文章中（无论是摘要还是全文）实现了句子级别的 0.81 的精度和 0.89 的召回率。我们还测量到，与简单的关键字搜索相比，使用 LiSA 可以使医学审查员收集和评估的相关文档数量增加 2.5 倍。为了提高可重用性，重点在于构建一个模块化管道，允许插入其他自然语言处理模块来丰富系统提供的结果，并将其扩展到其他用例。此外，还开发了一个轻量级可视化工具来分析和监测安全信号结果。
总的来说，本文提出的通用管道和可视化工具允许从文献中高效准确地监测严重药物不良反应，并可以轻松适应类似的药物警戒用例。为了促进可重复性并使其他研究受益，我们还共享了用于检测严重药物不良反应的第一个基准数据集。
