@inproceedings{1cf10b3d25da492eb9c3cb73a154e595,
title = "Staggered NLP-assisted refinement for clinical annotations of chronic disease events",
abstract = "Domain-specific annotations for NLP are often centered on real-world applications of text, and incorrect annotations may be particularly unacceptable. In medical text, the process of manual chart review (of a patient's medical record) is error-prone due to its complexity. We propose a staggered NLP-assisted approach to the refinement of clinical annotations, an interactive process that allows initial human judgments to be verified or falsified by means of comparison with an improving NLP system. We show on our internal Asthma Timelines dataset that this approach improves the quality of the human-produced clinical annotations.",
keywords = "Annotation refinement, Clinical text, Rule-based NLP, Staggered approaches",
author = "Wu, {Stephen T.} and Wi, {Chung Il} and Sunghwan Sohn and Hongfang Liu and Juhn, {Young J.}",
note = "Funding Information: Thanks to Yanshan Wang for subsequent work with this data set. This work was supported in part by NIH grants R21AI116839 and R01AI112590. Funding Information: data set. This work was supported in part by NIH grants; 10th International Conference on Language Resources and Evaluation, LREC 2016 ; Conference date: 23-05-2016 Through 28-05-2016",
year = "2016",
language = "English (US)",
series = "Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016",
publisher = "European Language Resources Association (ELRA)",
pages = "426--429",
editor = "Nicoletta Calzolari and Khalid Choukri and Helene Mazo and Asuncion Moreno and Thierry Declerck and Sara Goggi and Marko Grobelnik and Jan Odijk and Stelios Piperidis and Bente Maegaard and Joseph Mariani",
booktitle = "Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016",
}