RT Journal Article
T1 Testing contextualized word embeddings to improve NER in spanish clinical case narratives
A1 Akhtyamova, Liliya
A1 Martínez Fernández, Paloma
A1 Verspoor, Karin
A1 Cardiff, John
AB In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute by extracting relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patients' discharge summaries and radiology reports. The extracted information can help in health-related decision making processes. The Named Entity Recognition (NER) task, which detects important concepts in texts (e.g., diseases, symptoms, drugs, etc.), is crucial in the information extraction process yet has received little attention in languages other than English. In this work, we develop a deep learning-based NLP pipeline for biomedical entity extraction in Spanish clinical narratives. We explore the use of contextualized word embeddings, which incorporate context variation into word representations, to enhance named entity recognition in Spanish language clinical text, particularly of pharmacological substances, compounds, and proteins. Various combinations of word and sense embeddings were tested on the evaluation corpus of the PharmacoNER 2019 task, the Spanish Clinical Case Corpus (SPACCC). This data set consists of clinical case sections extracted from open access Spanish-language medical publications. Our study shows that our deep-learning-based system with domain-specific contextualized embeddings coupled with stacking of complementary embeddings yields superior performance over a system with integrated standard and general-domain word embeddings. With this system, we achieve performance competitive with the state-of-the-art.
PB IEEE
SN 2169-3536
YR 2020
FD 2020-08-24
LK https://hdl.handle.net/10016/38089
UL https://hdl.handle.net/10016/38089
LA eng
DS e-Archivo
RD 18 jul. 2024