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

By Ozlem Uzuner, Brett R. South, Shuying Shen, Scott L. DuVall. 2011

1,316 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “State-of-the-art named entity recognizers can automatically annotate text with high accuracy [177].”
Recurrent Neural Networks for Classifying Relations in Clinical Notes (2017, neutral): “One such challenge focused in part on identifying the relations that may hold between medical problems and treatments, between medical problems and tests, as well as between pairs of medical problems [13].”
Publicly Available Clinical BERT Embeddings (2019, methodology): “(Zhu et al., 2018) trains an ELMo model over a corpus of mixed clinical discharge summaries, clinical radiology notes and medically oriented wikipedia articles, then demonstrates improved performance on the i2b2 2010 task (Uzuner et al., 2011).”
Medical Question Answering for Clinical Decision Support (2016, methodology): “Our methodology for automatically recognizing medical concepts in clinical texts benefits from the general framework developed by the 2010 shared-task on Challenges in Natural Language Processing for Clinical Data [30] provided by the Informatics for Integrating Biology at he Bedside (i2b2) and the ”
Improving large language models for clinical named entity recognition via prompt engineering (2024, positive): “We leveraged 2 distinct clinical NER tasks as benchmarks, namely the 2010 i2b2 concept extraction task 30 and the nervous system disorder-related event extraction task.”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, background): “5 relation corpus [133], DDIExtraction relation corpora [55, 56], i2b2/VA corpus [1], AIMed [136], BioInfer [124], HPRD50 [137], IEPA”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, background): “However, subtle relationships hidden in notes remain difficult to extract due to the complexity of the language used and the lack of explicit semantic resources describing the relationships between clinical concepts [119,120].”