Department of Oncology, the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Expert Opin Drug Saf. 2023 Jul-Dec;22(8):659-668. doi: 10.1080/14740338.2023.2228197. Epub 2023 Jul 3.
Pharmacovigilance (PV) involves monitoring and aggregating adverse event information from a variety of data sources, including health records, biomedical literature, spontaneous adverse event reports, product labels, and patient-generated content like social media posts, but the most pertinent details in these sources are typically available in narrative free-text formats. Natural language processing (NLP) techniques can be used to extract clinically relevant information from PV texts to inform decision-making.
We conducted a non-systematic literature review by querying the PubMed database to examine the uses of NLP in drug safety and distilled the findings to present our expert opinion on the topic.
New NLP techniques and approaches continue to be applied for drug safety use cases; however, systems that are fully deployed and in use in a clinical environment remain vanishingly rare. To see high-performing NLP techniques implemented in the real setting will require long-term engagement with end users and other stakeholders and revised workflows in fully formulated business plans for the targeted use cases. Additionally, we found little to no evidence of extracted information placed into standardized data models, which should be a way to make implementations more portable and adaptable.
药物警戒（PV）涉及从各种数据源（包括健康记录、生物医学文献、自发不良事件报告、产品标签和社交媒体帖子等患者生成的内容）监测和汇总不良事件信息，但这些来源中最相关的细节通常以叙述性纯文本格式提供。自然语言处理（NLP）技术可用于从 PV 文本中提取临床相关信息，为决策提供信息。
我们通过查询 PubMed 数据库进行了非系统性文献综述，以检查 NLP 在药物安全中的应用，并将研究结果提炼出来，以表达我们对此主题的专家意见。
新的 NLP 技术和方法继续被应用于药物安全用例；然而，在临床环境中完全部署和使用的系统仍然非常罕见。要在实际环境中看到高性能的 NLP 技术的实施，需要与最终用户和其他利益相关者进行长期接触，并在针对目标用例的全面业务计划中修订工作流程。此外，我们几乎没有发现提取的信息被放入标准化数据模型中的证据，这应该是使实施更具可移植性和适应性的一种方式。
