ArticleBMC bioinformatics2016
NOBLE - Flexible concept recognition for large-scale biomedical natural language processing.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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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.
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Who cites it
44 citing papers in PubMed.
- Adverse drug event detection using natural language processing: A scoping review of supervised learning methods.PloS one · 2023Article
- An overview of biomedical entity linking throughout the years.Journal of biomedical informatics · 2023Review
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Clinical concept recognition: Evaluation of existing systems on EHRs.Frontiers in artificial intelligence · 2022Article
- Learning adaptive representations for entity recognition in the biomedical domain.Journal of biomedical semantics · 2021Article
- Transformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer.Journal of medical Internet research · 2020Article
- Formal representation of patients' care context data: the path to improving the electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Applying citizen science to gene, drug and disease relationship extraction from biomedical abstracts.Bioinformatics (Oxford, England) · 2020Article
- Combining lexical and context features for automatic ontology extension.Journal of biomedical semantics · 2020Article
- Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Article
- Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Building Cancer Diagnosis Text to OncoTree Mapping Pipelines for Clinical Sequencing Data Integration and Curation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data.JAMIA open · 2019Article
- Ensembles of natural language processing systems for portable phenotyping solutions.Journal of biomedical informatics · 2019Article
- Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records.Cancer research · 2019Review
- A tale of three subspecialties: Diagnosis recording patterns are internally consistent but Specialty-Dependent.JAMIA open · 2019Article
- Identifying incidental findings from radiology reports of trauma patients: An evaluation of automated feature representation methods.International journal of medical informatics · 2019Article
- Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes.JAMIA open · 2019Article
- Novel phenotype-disease matching tool for rare genetic diseases.Genetics in medicine : official journal of the American College of Medical Genetics · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundNatural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system's matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator.
resultsWe describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE's performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems.
conclusionNOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.
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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.