Overview of the 2022 n2c2 shared task on contextualized medication event extraction in clinical notes.
Overview of the 2022 n2c2 shared task on contextualized medication event extraction in clinical notes.
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2022 年 n2c2 临床记录中情境化用药事件提取共享任务概述。
DOI：
10.1016/j.jbi.2023.104432
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发表时间：
2023
影响因子：
4.5
通讯作者：
Uzuner,Özlem
中科院分区：
文献类型：
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作者：
Mahajan,Diwakar;Liang,JenniferJ;Tsou,Ching-Huei;Uzuner,Özlem
准确的用药史是提供优质医疗服务的基础，需要了解临床记录中记录的药物变化事件。然而，没有必要的临床背景下提取药物变化是不够的现实世界的application.MethodsTo解决这一需求，轨道1的2022年国家NLP临床挑战侧重于提取的背景下，记录在临床笔记中使用的上下文化用药事件数据集的药物变化。跟踪1由3个子任务组成：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、时间性、确定性和参与者（上下文）。参与者被允许参加任何一个或多个subtasks.ResultsA共32队与来自19个国家的参与者提交了共211个系统在所有子任务。大多数团队使用基于transformer的大型语言模型将NER制定为令牌分类任务，将事件和上下文制定为多类分类任务。总体而言，NER的性能在提交的系统中很高。然而，事件和上下文的表现要低得多，往往是由于间接陈述的变化事件没有明确的动作动词，事件需要进一步的文本线索的理解，和药物提到多个变化events.ConclusionsThis共享任务表明，虽然NLP研究药物提取相对成熟，对临床记录中围绕药物事件的上下文信息的理解仍然是一个开放的问题，需要进一步研究以实现支持真实的世界临床应用。
BackgroundAn 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.MethodsTo 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.ResultsA 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.ConclusionsThis 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.