IBM T.J. Watson Research Center, Yorktown Heights, NY, United States of America.
IBM T.J. Watson Research Center, Yorktown Heights, NY, United States of America.
J Biomed Inform. 2023 Aug;144:104432. doi: 10.1016/j.jbi.2023.104432. Epub 2023 Jun 24.
An accurate medication history, foundational for providing quality medical care, requires understanding of medication change events documented in clinical notes. However, extracting medication changes without the necessary clinical context is insufficient for real-world applications.
To address this need, Track 1 of the 2022 National NLP Clinical Challenges focused on extracting the context for medication changes documented in clinical notes using the Contextualized Medication Event Dataset. Track 1 consisted of 3 subtasks: extracting medication mentions from clinical notes (NER), determining whether a medication change is being discussed (Event), and determining the action, negation, temporality, certainty, and actor for any change events (Context). Participants were allowed to participate in any one or more of the subtasks.
A total of 32 teams with participants from 19 countries submitted a total of 211 systems across all subtasks. Most teams formulated NER as a token classification task and Event and Context as multi-class classification tasks, using transformer-based large language models. Overall, performance for NER was high across submitted systems. However, performance for Event and Context were much lower, often due to indirectly stated change events with no clear action verb, events requiring farther textual clues for understanding, and medication mentions with multiple change events.
This shared task showed that while NLP research on medication extraction is relatively mature, understanding of contextual information surrounding medication events in clinical notes is still an open problem requiring further research to achieve the end goal of supporting real-world clinical applications.
准确的用药史是提供高质量医疗服务的基础，需要理解临床记录中记录的用药变更事件。然而，提取没有必要临床上下文的用药变更对于实际应用来说是不够的。
为了解决这一需求，2022 年全国自然语言处理临床挑战的第 1 赛道专注于使用上下文药物事件数据集从临床记录中提取药物变更的上下文。第 1 赛道包括 3 个子任务：从临床记录中提取药物提及（NER），确定是否正在讨论药物变更（事件），以及确定任何变更事件的动作、否定、时态、确定性和参与者（上下文）。参与者可以参加一个或多个子任务。
共有来自 19 个国家的 32 个团队提交了总计 211 个系统，涵盖了所有子任务。大多数团队将 NER 制定为标记分类任务，将事件和上下文制定为多类分类任务，使用基于转换器的大型语言模型。总体而言，提交的系统在 NER 方面表现出色。然而，事件和上下文的表现要低得多，这通常是由于没有明确动作动词的间接陈述的变更事件、需要更深入的文本线索才能理解的事件以及具有多个变更事件的药物提及。
这项共享任务表明，虽然药物提取的自然语言处理研究相对成熟，但对临床记录中药物事件周围上下文信息的理解仍然是一个尚未解决的问题，需要进一步研究以实现支持实际临床应用的最终目标。
