Search Results for author:
Found 10 papers, 2 papers with code
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition
We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese.
Ranked #1 on
Named Entity Recognition (NER)
on SemClinBr
BiTeM at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models
Recent improvements in machine-reading technologies attracted much attention to automation problems and their possibilities.
DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models
The goal of this task is to locate and classify named entities in unstructured short complex texts in 11 different languages. After training a variety of contextual language models on the NER dataset, we used an ensemble strategy based on a majority vote to finalize our model.
Multilingual Named Entity Recognition
named-entity-recognition
+2
CT-ADE: An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
Adverse drug events (ADEs) significantly impact clinical research and public health, contributing to failures in clinical trials and leading to increased healthcare costs.
DS4DH at #SMM4H 2023: Zero-Shot Adverse Drug Events Normalization using Sentence Transformers and Reciprocal-Rank Fusion
This paper outlines the performance evaluation of a system for adverse drug event normalization, developed by the Data Science for Digital Health (DS4DH) group for the Social Media Mining for Health Applications (SMM4H) 2023 shared task 5.
Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events
Current approaches for clinical information extraction are inefficient in terms of computational costs and memory consumption, hindering their application to process large-scale electronic health records (EHRs).
DS4DH at TREC Health Misinformation 2021: Multi-Dimensional Ranking Models with Transfer Learning and Rank Fusion
The TREC Health Misinformation track focused on the development of retrieval methods that provide relevant, correct and credible information for health related searches on the Web.
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus
We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories.
Named entity recognition in chemical patents using ensemble of contextual language models
The results show that ensemble of contextualized language models can provide an effective method to extract information from chemical patents.
Contextualized French Language Models for Biomedical Named Entity Recognition
Our best approach achieved an F1 -measure of 66{\%} for symptoms and signs, and pathology categories, being top 1 for subtask 1.
