ArticlePloS one2019
Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Named Entity Recognition in Electronic Health Records: A Methodological Review.Healthcare informatics research · 2023Article
- Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models.Bioengineering (Basel, Switzerland) · 2023Article
- A BERT-Span model for Chinese named entity recognition in rehabilitation medicine.PeerJ. Computer science · 2023Article
- A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers.Frontiers in plant science · 2023Article
- Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition.Frontiers in neuroscience · 2023Article
- Trends in using deep learning algorithms in biomedical prediction systems.Frontiers in neuroscience · 2023Article
- Deep learning for medicinal plant species classification and recognition: a systematic review.Frontiers in plant science · 2023Review
- A Review of Recent Work in Transfer Learning and Domain Adaptation for Natural Language Processing of Electronic Health Records.Yearbook of medical informatics · 2021Review
- Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature.Briefings in bioinformatics · 2021Review
- Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study.PloS one · 2020Article
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Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN model combined with deep transfer learning is proposed as a potential solution of transferring knowledge and data augmentation to enhance NER performance with limited data. The proposed model has been evaluated using micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in the case of discharge datasets. For instance, for the case of discharge summary, the micro average F-score is improved by 2.55% and the overall accuracy is improved by 7.53%. For the case of progress notes, the micro average F-score and the overall accuracy are improved by 1.63% and 5.63%, respectively.
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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.