ArticleJournal of the American Medical Informatics Association : JAMIA2015
Evaluating the state of the art in disorder recognition and normalization of the clinical narrative.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
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- Use of Natural Language Processing to Identify Sexual and Reproductive Health Information in Clinical Text.Methods of information in medicine · 2023Article
- A scoping review of publicly available language tasks in clinical natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Exploring optimal granularity for extractive summarization of unstructured health records: Analysis of the largest multi-institutional archive of health records in Japan.PLOS digital health · 2022Article
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- SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks.Journal of biomedical semantics · 2022Article
- Clinical concept recognition: Evaluation of existing systems on EHRs.Frontiers in artificial intelligence · 2022Article
- Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach.PeerJ. Computer science · 2022Article
- Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets.Journal of biomedical informatics · 2021Article
- Leveraging network analysis to evaluate biomedical named entity recognition tools.Scientific reports · 2021Article
- Observational
- A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling.International journal of medical informatics · 2021Article
- Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Measuring Pain in Sickle Cell Disease using Clinical Text.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.PLoS computational biology · 2020Article
- BERT-based Ranking for Biomedical Entity Normalization.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Article
- Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries.BMC bioinformatics · 2019Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
Abstract
objectiveThe ShARe/CLEF eHealth 2013 Evaluation Lab Task 1 was organized to evaluate the state of the art on the clinical text in (i) disorder mention identification/recognition based on Unified Medical Language System (UMLS) definition (Task 1a) and (ii) disorder mention normalization to an ontology (Task 1b). Such a community evaluation has not been previously executed. Task 1a included a total of 22 system submissions, and Task 1b included 17. Most of the systems employed a combination of rules and machine learners. MATERIALS AND
methodsWe used a subset of the Shared Annotated Resources (ShARe) corpus of annotated clinical text--199 clinical notes for training and 99 for testing (roughly 180 K words in total). We provided the community with the annotated gold standard training documents to build systems to identify and normalize disorder mentions. The systems were tested on a held-out gold standard test set to measure their performance.
resultsFor Task 1a, the best-performing system achieved an F1 score of 0.75 (0.80 precision; 0.71 recall). For Task 1b, another system performed best with an accuracy of 0.59. DISCUSSION: Most of the participating systems used a hybrid approach by supplementing machine-learning algorithms with features generated by rules and gazetteers created from the training data and from external resources.
conclusionsThe task of disorder normalization is more challenging than that of identification. The ShARe corpus is available to the community as a reference standard for future studies.
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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.