A deep learning approach for medication disposition and corresponding attributes extraction.
A deep learning approach for medication disposition and corresponding attributes extraction.
复制标题
用于药物配置和相应属性提取的深度学习方法。
DOI：
10.1016/j.jbi.2023.104391
复制
发表时间：
2023
影响因子：
4.5
通讯作者：
Shi,Jianlin
中科院分区：
文献类型：
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作者：
Gan,Qiwei;Hu,Mengke;Peterson,KellyS;Eyre,Hannah;Alba,PatrickR;Bowles,AnnieE;Stanley,JohnathanC;DuVall,ScottL;Shi,Jianlin
本文总结了我们从临床笔记中提取药物和相应属性的方法，这是2022年国家自然语言处理（NLP）临床挑战（n2 c2）共享task.MethodsThe数据集使用情境化药物事件数据集（CMED）准备，包括来自296名患者的500条笔记。我们的系统由三个部分组成：药物命名实体识别（NER），事件分类（EC）和上下文分类（CC）。这三个组件是使用Transformer模型构建的，它们的体系结构和输入文本工程略有不同。A zero-shot learning solution for CC was also explored.ResultsOur best performance systems achieved micro-average F1 scores of 0.973，0.911，and 0.909 for the NER，EC，and CC，respectively.ConclusionIn this study，我们实现了一个基于深度学习的NLP系统，并证明了我们的方法（1）利用特殊的标记有助于我们的模型区分在同一上下文中提到的多种药物;（2）将单个药物的多个事件聚合到多个标签中可以提高我们模型的性能。
ObjectiveThis article summarizes our approach to extracting medication and corresponding attributes from clinical notes, which is the focus of track 1 of the 2022 National Natural Language Processing (NLP) Clinical Challenges(n2c2) shared task.MethodsThe dataset was prepared using Contextualized Medication Event Dataset (CMED), including 500 notes from 296 patients. Our system consisted of three components: medication named entity recognition (NER), event classification (EC), and context classification (CC). These three components were built using transformer models with slightly different architecture and input text engineering. A zero-shot learning solution for CC was also explored.ResultsOur best performance systems achieved micro-average F1 scores of 0.973, 0.911, and 0.909 for the NER, EC, and CC, respectively.ConclusionIn this study, we implemented a deep learning-based NLP system and demonstrated that our approach of (1) utilizing special tokens helps our model to distinguish multiple medications mentions in the same context; (2) aggregating multiple events of a single medication into multiple labels improves our model’s performance.