Medical Named Entity Recognition
14 papers with code • 2 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Medical Named Entity Recognition
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RadGraph
The QUAERO French Medical Corpus
Latest papers with no code
Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition
Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP).
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training
In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical records.
Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF
Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research.
Towards Building Automatic Medical Consultation System: Framework, Task and Dataset
In this paper, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and diagnosis-oriented interaction.
SINA-BERT: A Pre-Trained Language Model for Analysis of Medical Texts in Persian
We have released SINA-BERT, a language model pre-trained on BERT to address the lack of a high-quality Persian language model in the medical domain.
FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning
Since the labeled data in different platforms usually has some differences in entity type and annotation criteria, instead of constraining different platforms to share the same model, we decompose the medical NER model in each platform into a shared module and a private module.
CRAFT Shared Tasks 2019 Overview --- Integrated Structure, Semantics, and Coreference
As part of the BioNLP Open Shared Tasks 2019, the CRAFT Shared Tasks 2019 provides a platform to gauge the state of the art for three fundamental language processing tasks {---} dependency parse construction, coreference resolution, and ontology concept identification {---} over full-text biomedical articles.
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition
We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining.
Bootstrapping a Romanian Corpus for Medical Named Entity Recognition
Named Entity Recognition (NER) is an important component of natural language processing (NLP), with applicability in biomedical domain, enabling knowledge-discovery from medical texts.
Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language.
