ArticleJournal of the American Medical Informatics Association : JAMIA
Using rule-based natural language processing to improve disease normalization in biomedical text.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed.
- A robust natural language text-to-SQL generation framework with dynamic strategies based on LLMs.Scientific reports · 2026Article
- Paraphrase detection for Urdu language text using fine-tune BiLSTM framework.Scientific reports · 2025Article
- Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models.Journal of biomedical semantics · 2024Article
- PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking.International ACM SIGIR Conference on Research and Development in Information Retrieval. Annual International ACMSIGIR Conference on Research & Development in Information Retrieval · 2024Article
- Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records.Mayo Clinic proceedings. Digital health · 2024Article
- Mapping Vaccine Names in Clinical Trials to Vaccine Ontology using Cascaded Fine-Tuned Domain-Specific Language Models.Research square · 2023Article
- B-LBConA: a medical entity disambiguation model based on Bio-LinkBERT and context-aware mechanism.BMC bioinformatics · 2023Article
- Edge Weight Updating Neural Network for Named Entity Normalization.Neural processing letters · 2022Article
- Accurately Identifying Cerebroarterial Stenosis from Angiography Reports Using Natural Language Processing Approaches.Diagnostics (Basel, Switzerland) · 2022Article
- A simple neural vector space model for medical concept normalization using concept embeddings.Journal of biomedical informatics · 2022Article
- Automated medical chart review for breast cancer outcomes research: a novel natural language processing extraction system.BMC medical research methodology · 2022Article
- A Multitask Deep Learning Framework for DNER.Computational intelligence and neuroscience · 2022Article
- Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke.BMC medical informatics and decision making · 2021Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- Automatic Labeled Dialogue Generation for Nursing Record Systems.Journal of personalized medicine · 2020Article
- Normalizing Adverse Events using Recurrent Neural Networks with Attention.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- BERT-based Ranking for Biomedical Entity Normalization.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)-Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study.JMIR medical informatics · 2019Article
- Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries.BMC bioinformatics · 2019Article
- Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveIn order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the usefulness of natural language processing (NLP) as an adjunct to dictionary-based concept normalization.
methodsWe compared the performance of two biomedical concept normalization systems, MetaMap and Peregrine, on the Arizona Disease Corpus, with and without the use of a rule-based NLP module. Performance was assessed for exact and inexact boundary matching of the system annotations with those of the gold standard and for concept identifier matching.
resultsWithout the NLP module, MetaMap and Peregrine attained F-scores of 61.0% and 63.9%, respectively, for exact boundary matching, and 55.1% and 56.9% for concept identifier matching. With the aid of the NLP module, the F-scores of MetaMap and Peregrine improved to 73.3% and 78.0% for boundary matching, and to 66.2% and 69.8% for concept identifier matching. For inexact boundary matching, performances further increased to 85.5% and 85.4%, and to 73.6% and 73.3% for concept identifier matching.
conclusionsWe have shown the added value of NLP for the recognition and normalization of diseases with MetaMap and Peregrine. The NLP module is general and can be applied in combination with any concept normalization system. Whether its use for concept types other than disease is equally advantageous remains to be investigated.
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.