ArticleDatabase : the journal of biological databases and curation2016
Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion.
Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.Computational and structural biotechnology journal · 2024Article
- Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models.Journal of biomedical semantics · 2024Article
- Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes.Database : the journal of biological databases and curation · 2024Article
- Mapping Vaccine Names in Clinical Trials to Vaccine Ontology using Cascaded Fine-Tuned Domain-Specific Language Models.Research square · 2023Article
- A simple neural vector space model for medical concept normalization using concept embeddings.Journal of biomedical informatics · 2022Article
- Unified Medical Language System resources improve sieve-based generation and Bidirectional Encoder Representations from Transformers (BERT)-based ranking for concept normalization.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature.Database : the journal of biological databases and curation · 2019Article
- MER: a shell script and annotation server for minimal named entity recognition and linking.Journal of cheminformatics · 2018Article
- Comparison of MetaMap and cTAKES for entity extraction in clinical notes.BMC medical informatics and decision making · 2018Article
- Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients.Journal of biomedical informatics · 2017Article
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4 authors.
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Abstract
The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13.Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract.
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