IBM T.J. Watson Research Center, Yorktown Heights, NY.
AMIA Annu Symp Proc. 2022 Feb 21;2021:833-842. eCollection 2021.
Understanding medication events in clinical narratives is essential to achieving a complete picture of a patient's medication history. While prior research has explored identification of medication changes in clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation. Here, we present a framework to capture multi-dimensional context of medication changes documented in clinical notes. We define specific contextual aspects pertinent to medication change events (i.e. Action, Negation, Temporality, Certainty, and Actor), describe the annotation process and challenges encountered while creating the dataset, and explore models based on state-of-the-art transformers to automate the task. The resulting dataset, Contextualized Medication Event Dataset (CMED), consisting of 9,013 medications annotated over 500 clinical notes, will be released to the community as a shared task in 2021-2022.
理解临床叙述中的用药事件对于全面了解患者的用药史至关重要。虽然之前的研究已经探讨了在临床记录中识别用药变化,但由于临床文档的纵向和叙述性质,仅提取用药变化而没有必要的临床背景信息对于实际应用(如用药时间线生成和用药核对)来说是不够的。在这里,我们提出了一个框架来捕捉临床记录中记录的用药变化的多维上下文。我们定义了与用药变化事件相关的特定上下文方面(即动作、否定、时态、确定性和执行者),描述了创建数据集时的注释过程和遇到的挑战,并探索了基于最先进的转换器的模型来实现自动化任务。由此产生的数据集,即上下文用药事件数据集(CMED),包含 9013 种药物,涵盖 500 多份临床记录,将在 2021-2022 年作为共享任务向社区发布。