NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction

Lukas Lange; Xiang Dai; Heike Adel; Jannik Strötgen

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arXiv · 2010.12322

NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction

Lukas Lange·Xiang Dai·Heike Adel·Jannik Strötgen

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Abstract

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.

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Lukas Lange, Xiang Dai, Heike Adel, Jannik Strötgen. 2020-10-23. NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction. https://arxiv.org/abs/2010.12322

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