Spa-neg: An Approach for Negation Detection in Clinical Text Written in Spanish

Pabón, Oswaldo Solarte; Menasalvas, Ernestina; Rodríguez‐González, Alejandro

doi:10.1007/978-3-030-45385-5_29

Cited by 3 publications

(1 citation statement)

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“…The proposed approach automatically represents text features using word embeddings and contextual embeddings, and uses them to detect uncertainty and negation. This is an advantage over previous proposals that require complex hand-crafted rules ( Costumero et al, 2014 ; Cotik et al, 2016 ; Solarte-Pabón, Menasalvas & Rodriguez-González, 2020 ) or a considerable set of hand-crafted features ( Lima Lopez et al, 2020 ) to obtain comparable results.…”

Section: Discussionmentioning

confidence: 86%

Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Pabón

Montenegro

Torrente

et al. 2022

PeerJ Computer Science

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Detecting negation and uncertainty is crucial for medical text mining applications; otherwise, extracted information can be incorrectly identified as real or factual events. Although several approaches have been proposed to detect negation and uncertainty in clinical texts, most efforts have focused on the English language. Most proposals developed for Spanish have focused mainly on negation detection and do not deal with uncertainty. In this paper, we propose a deep learning-based approach for both negation and uncertainty detection in clinical texts written in Spanish. The proposed approach explores two deep learning methods to achieve this goal: (i) Bidirectional Long-Short Term Memory with a Conditional Random Field layer (BiLSTM-CRF) and (ii) Bidirectional Encoder Representation for Transformers (BERT). The approach was evaluated using NUBES and IULA, two public corpora for the Spanish language. The results obtained showed an F-score of 92% and 80% in the scope recognition task for negation and uncertainty, respectively. We also present the results of a validation process conducted using a real-life annotated dataset from clinical notes belonging to cancer patients. The proposed approach shows the feasibility of deep learning-based methods to detect negation and uncertainty in Spanish clinical texts. Experiments also highlighted that this approach improves performance in the scope recognition task compared to other proposals in the biomedical domain.

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Section: Discussionmentioning

confidence: 86%