2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

Uzuner, Özlem; South, Brett R.; Shen, Shuying; DuVall, Scott L.

doi:10.1136/amiajnl-2011-000203

Cited by 910 publications

(817 citation statements)

References 15 publications

Supporting

Mentioning

758

Contrasting

Unclassified

Order By: Relevance

“…Research challenges have also fuelled the annotation of resources or enrichment of available texts. Well-known corpora come from the i2b2 challenges (Uzuner et al 2010(Uzuner et al , 2011Sun et al 2013), SemEval (Bethard et al 2016) and the Shared Annotated Resources (ShARe)/CLEF eHealth labs. 8 Overall, two levels of annotations have been applied in clinical texts.…”

Section: Introductionmentioning

confidence: 99%

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

Campillos

Deléger

Grouin

et al. 2017

Lang Resources & Evaluation

View full text Add to dashboard Cite

Quality annotated resources are essential for Natural Language Processing. The objective of this work is to present a corpus of clinical narratives in French annotated for linguistic, semantic and structural information, aimed at clinical information extraction. Six annotators contributed to the corpus annotation, using a comprehensive annotation scheme covering 21 entities, 11 attributes and 37 relations. All annotators trained on a small, common portion of the corpus before proceeding independently. An automatic tool was used to produce entity and attribute pre-annotations. About a tenth of the corpus was doubly annotated and annotation differences were resolved in consensus meetings. To ensure annotation consistency throughout the corpus, we devised harmonization tools to automatically identify annotation differences to be addressed to improve the overall corpus quality. The annotation project spanned over 24 months and resulted in a corpus comprising 500 documents (148,476 tokens) annotated with 44,740 entities and 26,478 relations. The average inter-annotator agreement is 0.793 F-measure for entities and 0.789 for relations. The performance of the pre-annotation tool for entities reached 0.814 F-measure when sufficient training data was available. The performance of our entity pre-annotation tool shows the value of the corpus to build and evaluate information extraction methods. In addition, we introduced harmonization methods that further improved the quality of annotations in the corpus.Electronic supplementary material The online version of this article

show abstract

Section: Introductionmentioning

confidence: 99%

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

Campillos

Deléger

Grouin

et al. 2017

Lang Resources & Evaluation

View full text Add to dashboard Cite

show abstract

“…On the other hand, corpora of clinical text drawn from EHRs are rare, due to privacy and confidentiality concerns, but also because of the time-consuming, expensive and tedious nature of producing high quality annotations, which are reliant on the expertise of domain experts (Uzuner et al, 2011). A small number of corpora, however, have been made available, mainly in the context of shared task challenges, which aim to encourage the development of information extraction (IE) systems.…”

Section: Related Workmentioning

confidence: 99%

Building a semantically annotated corpus for congestive heart and renal failure from clinical records and the literature

Alnazzawi¹,

Thompson²,

Ananiadou³

2014

Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

View full text Add to dashboard Cite

Narrative information in Electronic Health Records (EHRs) and literature articles contains a wealth of clinical information about treatment, diagnosis, medication and family history. This often includes detailed phenotype information for specific diseases, which in turn can help to identify risk factors and thus determine the susceptibility of different patients. Such information can help to improve healthcare applications, including Clinical Decision Support Systems (CDS). Clinical text mining (TM) tools can provide efficient automated means to extract and integrate vital information hidden within the vast volumes of available text. Development or adaptation of TM tools is reliant on the availability of annotated training corpora, although few such corpora exist for the clinical domain. In response, we have created a new annotated corpus (PhenoCHF), focussing on the identification of phenotype information for a specific clinical sub-domain, i.e., congestive heart failure (CHF). The corpus is unique in this domain, in its integration of information from both EHRs (300 discharge summaries) and literature articles (5 full-text papers). The annotation scheme, whose design was guided by a domain expert, includes both entities and relations pertinent to CHF. Two further domain experts performed the annotation, resulting in high quality annotation, with agreement rates up to 0.92 F-Score.

show abstract

“…Most information extraction efforts focused on phenotyping and chart abstraction improvement [125], research subjects recruitment and cohort identification for retrospective studies, and patient identification for improved treatment and follow-up. The extraction of phenotypes and other types of information include diseases and problems, investigations, treatments, combined in the 4th i2b2 NLP challenge [126], or medication details for example [127]. Various data and attribute values were extracted to support peripheral artery disease and heart failure research in the eMERGE network [128], and to support obesity research [129].…”

Section: F Extraction Of Information From Unstructured Clinical Datamentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

View full text Add to dashboard Cite

SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

show abstract

2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

Cited by 910 publications

References 15 publications

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

Building a semantically annotated corpus for congestive heart and renal failure from clinical records and the literature

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Contact Info

Product

Resources

About