1 code implementation • NAACL (ClinicalNLP) 2022 • Matías Rojas, Jocelyn Dunstan, Fabián Villena
To validate the quality of the contextual representations retrieved from our model, we tested them on four named entity recognition datasets belonging to the clinical and biomedical domains.
2 code implementations • COLING 2022 • Matias Rojas, Felipe Bravo-Marquez, Jocelyn Dunstan
Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories.
Ranked #1 on Nested Named Entity Recognition on Chilean Waiting List (Micro F1 (Exact Span) metric)
no code implementations • EMNLP (ClinicalNLP) 2020 • Pablo Báez, Fabián Villena, Matías Rojas, Manuel Durán, Jocelyn Dunstan
The best results were achieved by using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group.
no code implementations • SMM4H (COLING) 2022 • Matias Rojas, Jose Barros, Kinan Martin, Mauricio Araneda-Hernandez, Jocelyn Dunstan
The proposed model is an architecture based on the FLERT approach.
1 code implementation • 2 Aug 2023 • Jeremias Garay, Jocelyn Dunstan, Sergio Uribe, Francisco Sahli Costabal
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known and scatter measurements are available.
no code implementations • 9 Jul 2023 • Fabián Villena, Matías Rojas, Felipe Arias, Jorge Pacheco, Paulina Vera, Jocelyn Dunstan
This system could be a support tool for health professionals, optimizing the coding and management process.
1 code implementation • ACM Transactions on Computing for Healthcare 2022 • Pablo Báez, Felipe Bravo-Marquez, Jocelyn Dunstan, Matías Rojas, Fabián Villena
The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community.