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

arXiv: https://arxiv.org/abs/2010.12322 · HTML: https://mlsanity.com/paper/2010.12322/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain](https://mlsanity.com/paper/2112.08754/) (2021)
- [Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting](https://mlsanity.com/paper/2109.08597/) (2021)

## Abstract (arXiv)

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.