Extracting medication information from clinical text

By Ozlem Uzuner, Imre Solti, Eithon Cadag. J. Am. Medical Informatics Assoc., 2010

515 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3eff9e60cb12777f30747698e63294722348e43d

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Citations in the corpus

Using electronic health records to drive discovery in disease genomics (2011, background): “Similar or better performance has been shown for attributes such as smoking and medication histor”
Large language models are few-shot clinical information extractors (2022, methodology): “The anno-tators closely followed the schema from the 2009 i2b2 medication challenge (Uzuner et al., 2010), with small deviations explained in Appendix B.4.”
Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions (2011, unknown): “and the 2011 TREC information retrieval shared task involving retrieval of clinical cases from narrative records.”
Clinical Natural Language Processing in languages other than English: opportunities and challenges (2018, methodology): “However, there are currently no sharable clinical datasets comparable to the i2b2 datasets [142, 143], the ShARe corpus [144], the THYME corpus [145, 146] or the MIMIC corpus [147] in languages other than English except the Turku Clinical TreeBank and PropBank [32, 38, 148] in Finnish and the small ”
Natural language processing: an introduction (2011, unknown): “IE and relationship extraction have been themes of several i2b2/VA NLP challenges.”
The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions (2013, background): “Other corpora focus on the annotation of clinical texts such as discharge summaries [27], patient records [20] or clinical notes [28].”
emrQA: A Large Corpus for Question Answering on Electronic Medical Records (2018, methodology): “We align our entity types to those deﬁned in the i2b2 concept extraction tasks (Uzuner et al., 2010a, 2011) - problem, test, treatment, mode and medication .”
A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries (2011, unknown): “the training data The evaluation was carried out using a script provided by i2b2/ VA organizers, which can report micro-averaged Precision, Recall, and F-measure for all concepts using both exact and inexact matching methods.(33) The exact matching criterion requires that the starting and ending off”