Evaluating temporal relations in clinical text: 2012 i2b2 Challenge

By Weiyi Sun, Anna Rumshisky, Ozlem Uzuner. 2013

509 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Publicly Available Clinical BERT Embeddings (2019, methodology): “…and uses complex task-speciﬁc models to yield improvements over both traditional embeddings and ELMo embeddings on the i2b2 2010 and 2012 tasks (Sun et al., 2013b,a) and the SemEval 2014 task 7 (Pradhan et al., 2014) and 2015 task 14 (El-hadad et al.) tasks, establishing new state-of-the-art…”
Unsupervised entity and relation extraction from clinical records in Italian (2016, background): “On the other hand, the costs in time and money required by the annotation justifies the proposal of unsupervised approaches for relation extraction [26–35].”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, background): “'), and identifying temporal relationships from text mentions of clinical events (for example, 'the hemorrhage began a week after starting warfarin') [73].”
Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review (2017, background): “The organizers of the 2012 i2b2 challenge on temporal relations concluded that even state of the art techniques did not provide good performance for temporal relation identification [3].”
SemEval-2015 Task 6: Clinical TempEval (2015, background): “This follows recent interest in temporal information extraction for the clinical domain, e.g., the i2b2 2012 shared task (Sun et al., 2013), and broad-ens our understanding of the language of time beyond newswire expressions and structure.”
Overview of BioNLP Shared Task 2013 (2013, background): “…BioCreative (Critical Assessment of Information Extraction in Biology) (Arighi et al. , 2011), DDIExtraction (Extraction of Drug-Drug Interactions from biomedical texts) (Segura-Bedmar et al ., 2011) and i2b2 (Informatics for Integrating Biology and the Bedside) Shared-Tasks (Sun et al. , 2013).”
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference (2020, unknown): “The I2B2-2012 challenge corpus (Sun, Rumshisky, and Uzuner 2013) consists of 310 discharge summaries.”
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, unknown): “This instability is more matters for Healthcare than other general applications.”
Neural Temporal Relation Extraction (2017, background): “The vast majority of systems in temporal information extraction challenges, such as the i2b2 (Sun et al., 2013) and Clinical TempEval tasks (Bethard et al., 2015; Bethard et al., 2016), used classiﬁers with a large number of manually engineered features.”
Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction (2020, unknown): “In the clinical domain, one of the earliest event temporal datasets was provided in the 2012 Informatics for Integrating Biology and the Bedside (i2b2) Challenge on NLP for Clinical Records (Sun et al., 2013).”
A large language model for electronic health records (2022, methodology): “54 We used three benchmark datasets developed by the 2010 i2b2 challenge 39 , 2012 i2b2 challenge 40 , and 2018 n2c2 challenge 41 to evaluate GatorTron models focusing on identifying important medical concepts (e.g., medications, adverse drug events, treatments) from clinical text.”