[HTML][HTML] Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction
Abstract Background and Objectives: Named Entity Recognition (NER) and Relation
Extraction (RE) are two of the most studied tasks in biomedical Natural Language …
Extraction (RE) are two of the most studied tasks in biomedical Natural Language …
End-to-end joint entity extraction and negation detection for clinical text
Negative medical findings are prevalent in clinical reports, yet discriminating them from
positive findings remains a challenging task for information extraction. Most of the existing …
positive findings remains a challenging task for information extraction. Most of the existing …
[HTML][HTML] Neural negated entity recognition in Spanish electronic health records
S Santiso, A Pérez, A Casillas, M Oronoz - Journal of biomedical informatics, 2020 - Elsevier
This work deals with negation detection in the context of clinical texts. Negation detection is
a key for decision support systems since negated events (detection of absence of some …
a key for decision support systems since negated events (detection of absence of some …
[HTML][HTML] On the use of knowledge transfer techniques for biomedical named entity recognition
Biomedical named entity recognition (BioNER) is a preliminary task for many other tasks, eg,
relation extraction and semantic search. Extracting the text of interest from biomedical …
relation extraction and semantic search. Extracting the text of interest from biomedical …
[HTML][HTML] A two-stage deep learning approach for extracting entities and relationships from medical texts
V Suárez-Paniagua, RMR Zavala… - Journal of biomedical …, 2019 - Elsevier
This work presents a two-stage deep learning system for Named Entity Recognition (NER)
and Relation Extraction (RE) from medical texts. These tasks are a crucial step to many …
and Relation Extraction (RE) from medical texts. These tasks are a crucial step to many …
Improving biomedical named entity recognition through transfer learning and asymmetric tri-training
M Bhattacharya, S Bhat, S Tripathy, A Bansal… - Procedia Computer …, 2023 - Elsevier
Today, electronic health records have turned into prime sources of information for physicians
looking after their patients. EHRs and computerized patient data resources have expedited …
looking after their patients. EHRs and computerized patient data resources have expedited …
Improving named entity recognition for biomedical and patent data using bi-LSTM deep neural network models
The daily exponential increase of biomedical information in scientific literature and patents is
a main obstacle to foster advances in biomedical research. A fundamental step hereby is to …
a main obstacle to foster advances in biomedical research. A fundamental step hereby is to …
[HTML][HTML] Named entity recognition and relation detection for biomedical information extraction
The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …
Joint entity extraction and assertion detection for clinical text
Negative medical findings are prevalent in clinical reports, yet discriminating them from
positive findings remains a challenging task for information extraction. Most of the existing …
positive findings remains a challenging task for information extraction. Most of the existing …
Word embeddings for negation detection in health records written in Spanish
S Santiso, A Casillas, A Pérez, M Oronoz - Soft Computing, 2019 - Springer
This work focuses on the creation of a system to detect negated medical entities in electronic
health records (EHRs) written in Spanish. The importance of this task rests on the influence …
health records (EHRs) written in Spanish. The importance of this task rests on the influence …