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本文引用的文献

1
Automatic Methods to Extract Prescription Status Quality Measures from Unstructured Health Records.从非结构化健康记录中提取处方状态质量指标的自动化方法。
Stud Health Technol Inform. 2019 Aug 21;264:15-19. doi: 10.3233/SHTI190174.
2
Learning to detect and understand drug discontinuation events from clinical narratives.从临床叙述中学习检测和理解药物停用事件。
J Am Med Inform Assoc. 2019 Oct 1;26(10):943-951. doi: 10.1093/jamia/ocz048.
3
Designing a medication timeline for patients and physicians.为患者和医生设计用药时间表。
J Am Med Inform Assoc. 2019 Feb 1;26(2):95-105. doi: 10.1093/jamia/ocy143.
4
Using natural language processing methods to classify use status of dietary supplements in clinical notes.使用自然语言处理方法对临床记录中的膳食补充剂使用状态进行分类。
BMC Med Inform Decis Mak. 2018 Jul 23;18(Suppl 2):51. doi: 10.1186/s12911-018-0626-6.
5
A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes.一种用于联合预测临床记录中医疗事件的存在和时期断言的混合神经网络模型。
AMIA Annu Symp Proc. 2018 Apr 16;2017:1149-1158. eCollection 2017.
6
Classification of Use Status for Dietary Supplements in Clinical Notes.临床记录中膳食补充剂使用状态的分类
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2016 Dec;2016:1054-1061. doi: 10.1109/BIBM.2016.7822668. Epub 2017 Jan 19.
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Indexed Pain Journals.索引疼痛期刊。
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Creation of a new longitudinal corpus of clinical narratives.创建一个新的临床叙事纵向语料库。
J Biomed Inform. 2015 Dec;58 Suppl(Suppl):S6-S10. doi: 10.1016/j.jbi.2015.09.018. Epub 2015 Oct 1.
9
Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents.临床叙述性文档中心力衰竭药物检测与处方状态分类
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Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1.用于纵向临床记录去识别化的自动化系统:2014年i2b2/德克萨斯大学健康科学中心共享任务赛道1概述
J Biomed Inform. 2015 Dec;58 Suppl(Suppl):S11-S19. doi: 10.1016/j.jbi.2015.06.007. Epub 2015 Jul 28.

理解临床叙事中药物变更事件的临床背景。

Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives.

机构信息

IBM T.J. Watson Research Center, Yorktown Heights, NY.

出版信息

AMIA Annu Symp Proc. 2022 Feb 21;2021:833-842. eCollection 2021.

PMID:35308981
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8861744/
Abstract

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.

摘要

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。