论文标题
NLNDE在cantemist：临床概念提取的神经序列标记和解析方法
NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction
论文作者
论文摘要
临床信息的识别和归一化，例如肿瘤形态，是一个重要但复杂的过程，由多个子任务组成。在本文中，我们描述了我们的概括者共享任务的系统，该任务能够使用神经序列标签和解析方法从西班牙电子健康记录中提取，归一化和对ICD代码进行排名。我们最好的系统分别实现了三个任务的85.3 F1、76.7 F1和77.0地图。
The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks. In this paper, we describe our system for the CANTEMIST shared task, which is able to extract, normalize and rank ICD codes from Spanish electronic health records using neural sequence labeling and parsing approaches with context-aware embeddings. Our best system achieves 85.3 F1, 76.7 F1, and 77.0 MAP for the three tasks, respectively.
