[HTML][HTML] A clinical named entity recognition model using pretrained word embedding and deep neural networks
Abstract Clinical Named Entity Recognition (NER) within Electronic Medical Records
(EMRs) has seen substantial research attention. Since much clinical information resides in …
(EMRs) has seen substantial research attention. Since much clinical information resides in …
Models and techniques for domain relation extraction: a survey
J Wang, K Yue, L Duan - Journal of Data Science and …, 2023 - ojs.bonviewpress.com
As the significant subtask of information extraction, relation extraction (RE) aims to identify
and classify semantic relations between pairs of entities and is widely adopted as the …
and classify semantic relations between pairs of entities and is widely adopted as the …
[HTML][HTML] SPBERE: Boosting span-based pipeline biomedical entity and relation extraction via entity information
C Yang, J Deng, X Chen, Y An - Journal of Biomedical Informatics, 2023 - Elsevier
Triplet extraction is one of the fundamental tasks in biomedical text mining. Compared with
traditional pipeline approaches, joint methods can alleviate the error propagation problem …
traditional pipeline approaches, joint methods can alleviate the error propagation problem …
[HTML][HTML] Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records
B Guo, H Liu, L Niu - Frontiers in Neuroscience, 2023 - frontiersin.org
Introduction Medical images and signals are important data sources in the medical field, and
they contain key information such as patients' physiology, pathology, and genetics …
they contain key information such as patients' physiology, pathology, and genetics …
A survey on recent advances in named entity recognition
Named Entity Recognition seeks to extract substrings within a text that name real-world
objects and to determine their type (for example, whether they refer to persons or …
objects and to determine their type (for example, whether they refer to persons or …
Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models
M Iscoe, V Socrates, A Gilson, L Chi… - Academic …, 2023 - Wiley Online Library
Background Natural language processing (NLP) tools including recently developed large
language models (LLMs) have myriad potential applications in medical care and research …
language models (LLMs) have myriad potential applications in medical care and research …
[HTML][HTML] Advances in monolingual and crosslingual automatic disability annotation in Spanish
Background Unlike diseases, automatic recognition of disabilities has not received the same
attention in the area of medical NLP. Progress in this direction is hampered by obstacles like …
attention in the area of medical NLP. Progress in this direction is hampered by obstacles like …
[HTML][HTML] Improving Medical Entity Recognition in Spanish by Means of Biomedical Language Models
Named Entity Recognition (NER) is an important task used to extract relevant information
from biomedical texts. Recently, pre-trained language models have made great progress in …
from biomedical texts. Recently, pre-trained language models have made great progress in …
[PDF][PDF] Bridging the Gap: A Hybrid Approach to Medical Relation Extraction Using Pretrained Language Models and Traditional Machine Learning
Feature engineering can be time-consuming and challenging, requiring expertise in Natural
Language Processing (NLP) techniques and methods. The objective of this study was to …
Language Processing (NLP) techniques and methods. The objective of this study was to …
Structured Named Entity Recognition (NER) in Biomedical Texts Using Pre-Trained Language Models
The field of Natural Language Processing (NLP) has witnessed remarkable progress in
recent years, particularly in the domain of biomedical text analysis. Named Entity …
recent years, particularly in the domain of biomedical text analysis. Named Entity …