[HTML][HTML] A study of neural word embeddings for named entity recognition in clinical text
Abstract Clinical Named Entity Recognition (NER) is a critical task for extracting important
patient information from clinical text to support clinical and translational research. This study …
patient information from clinical text to support clinical and translational research. This study …
[PDF][PDF] Clinical abbreviation disambiguation using neural word embeddings
This study examined the use of neural word embeddings for clinical abbreviation
disambiguation, a special case of word sense disambiguation (WSD). We investigated three …
disambiguation, a special case of word sense disambiguation (WSD). We investigated three …
Clinical concept and relation extraction using prompt-based machine reading comprehension
Objective To develop a natural language processing system that solves both clinical concept
extraction and relation extraction in a unified prompt-based machine reading …
extraction and relation extraction in a unified prompt-based machine reading …
Clinical named entity recognition and relation extraction using natural language processing of medical free text: A systematic review
Abstract Background Natural Language Processing (NLP) applications have developed
over the past years in various fields including its application to clinical free text for named …
over the past years in various fields including its application to clinical free text for named …
Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting
Objective To develop a natural language processing system that identifies relations of
medications with adverse drug events from clinical narratives. This project is part of the 2018 …
medications with adverse drug events from clinical narratives. This project is part of the 2018 …
Fedner: Privacy-preserving medical named entity recognition with federated learning
Medical named entity recognition (NER) has wide applications in intelligent healthcare.
Sufficient labeled data is critical for training accurate medical NER model. However, the …
Sufficient labeled data is critical for training accurate medical NER model. However, the …
Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions
J Choi, K Bang, S Jang, J Choi, J Ordonez… - Journal of Materials …, 2023 - pubs.rsc.org
Large-scale and openly available material science databases are mainly composed of
computer simulation results rather than experimental data. Some examples include the …
computer simulation results rather than experimental data. Some examples include the …
A multiclass classification method based on deep learning for named entity recognition in electronic medical records
Research of named entity recognition (NER) on electrical medical records (EMRs) focuses
on verifying whether methods to NER in traditional texts are effective for that in EMRs, and …
on verifying whether methods to NER in traditional texts are effective for that in EMRs, and …
[HTML][HTML] Identifying adverse drug event information in clinical notes with distributional semantic representations of context
For the purpose of post-marketing drug safety surveillance, which has traditionally relied on
the voluntary reporting of individual cases of adverse drug events (ADEs), other sources of …
the voluntary reporting of individual cases of adverse drug events (ADEs), other sources of …
Legal document retrieval using document vector embeddings and deep learning
K Sugathadasa, B Ayesha, N de Silva… - … : Proceedings of the …, 2019 - Springer
Abstract Domain specific information retrieval process has been a prominent and ongoing
research in the field of natural language processing. Many researchers have incorporated …
research in the field of natural language processing. Many researchers have incorporated …