基于预训练语言模型和统一医学语义类型的跨领域德语医学命名实体识别

Siting Liang, Mareike Hartmann, Daniel Sonntag
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引用次数: 0

摘要

从临床文本中提取信息有可能促进临床研究和个性化临床护理,但为每组目标任务注释大量数据是令人望而却步的。我们提出了一个德国医学命名实体识别(NER)系统,能够跨领域的知识转移。该系统建立在预先训练的德语语言模型和标记级二元分类器的基础上,使用来自统一医学语言系统(UMLS)的语义类型作为实体标签来识别输入文本中相应的实体范围。为了提高系统的性能和鲁棒性,我们使用包含UMLS语义术语注释的医学文献语料库对其进行预训练。我们在零针和少针设置下从不同诊所获得的两个德语注释数据集上评估了该系统的有效性。结果表明,我们的方法在准确率方面优于任务特定条件随机场(CRF)分类器。我们的工作有助于开发强大和透明的德国医学NER模型,可以支持从各种临床文本中提取信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-domain German Medical Named Entity Recognition using a Pre-Trained Language Model and Unified Medical Semantic Types
Information extraction from clinical text has the potential to facilitate clinical research and personalized clinical care, but annotating large amounts of data for each set of target tasks is prohibitive. We present a German medical Named Entity Recognition (NER) system capable of cross-domain knowledge transferring. The system builds on a pre-trained German language model and a token-level binary classifier, employing semantic types sourced from the Unified Medical Language System (UMLS) as entity labels to identify corresponding entity spans within the input text. To enhance the system’s performance and robustness, we pre-train it using a medical literature corpus that incorporates UMLS semantic term annotations. We evaluate the system’s effectiveness on two German annotated datasets obtained from different clinics in zero- and few-shot settings. The results show that our approach outperforms task-specific Condition Random Fields (CRF) classifiers in terms of accuracy. Our work contributes to developing robust and transparent German medical NER models that can support the extraction of information from various clinical texts.
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