ArticleBMC bioinformatics2017
Long short-term memory RNN for biomedical named entity recognition.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media.Scientific reports · 2025Article
- A narrative review of deep learning in thyroid imaging: current progress and future prospects.Quantitative imaging in medicine and surgery · 2024Review
- Drug-disease association prediction with literature based multi-feature fusion.Frontiers in pharmacology · 2023Article
- Biomedical named entity recognition based on fusion multi-features embedding.Technology and health care : official journal of the European Society for Engineering and Medicine · 2023Article
- A Gated Recurrent Unit based architecture for recognizing ontology concepts from biological literature.BioData mining · 2022Article
- Development and clinical application of an electronic health record quality control system for pulmonary aspergillosis based on guidelines and natural language processing technology.Journal of thoracic disease · 2022Article
- Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts.BMC bioinformatics · 2022Article
- Automatic Classification of Cancer Pathology Reports: A Systematic Review.Journal of pathology informatics · 2022Article
- Mathematical processing of trading strategy based on long short-term memory neural network model.Frontiers in computational neuroscience · 2022Article
- Multi-task learning for Chinese clinical named entity recognition with external knowledge.BMC medical informatics and decision making · 2021Article
- BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.Journal of biomedical semantics · 2021Article
- The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities.Sensors (Basel, Switzerland) · 2021Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network.Scientific reports · 2021Article
- How Do Machines Learn? Artificial Intelligence as a New Era in Medicine.Journal of personalized medicine · 2021Review
- Article
- Learning Tasks of Pediatric Providers from Electronic Health Record Audit Logs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- A hybrid approach for named entity recognition in Chinese electronic medical record.BMC medical informatics and decision making · 2019Article
- A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records.BMC medical informatics and decision making · 2019Article
- Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture.Computational and structural biotechnology journal · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBiomedical named entity recognition(BNER) is a crucial initial step of information extraction in biomedical domain. The task is typically modeled as a sequence labeling problem. Various machine learning algorithms, such as Conditional Random Fields (CRFs), have been successfully used for this task. However, these state-of-the-art BNER systems largely depend on hand-crafted features.
resultsWe present a recurrent neural network (RNN) framework based on word embeddings and character representation. On top of the neural network architecture, we use a CRF layer to jointly decode labels for the whole sentence. In our approach, contextual information from both directions and long-range dependencies in the sequence, which is useful for this task, can be well modeled by bidirectional variation and long short-term memory (LSTM) unit, respectively. Although our models use word embeddings and character embeddings as the only features, the bidirectional LSTM-RNN (BLSTM-RNN) model achieves state-of-the-art performance - 86.55% F1 on BioCreative II gene mention (GM) corpus and 73.79% F1 on JNLPBA 2004 corpus.
conclusionsOur neural network architecture can be successfully used for BNER without any manual feature engineering. Experimental results show that domain-specific pre-trained word embeddings and character-level representation can improve the performance of the LSTM-RNN models. On the GM corpus, we achieve comparable performance compared with other systems using complex hand-crafted features. Considering the JNLPBA corpus, our model achieves the best results, outperforming the previously top performing systems. The source code of our method is freely available under GPL at https://github.com/lvchen1989/BNER .
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.