Active learning reduces annotation time for clinical concept extraction.

Journal: International journal of medical informatics

Published Date: Aug 5, 2017

Abstract

OBJECTIVE: To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation.

Authors

Mahnoosh Kholghi

Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia.
Laurianne Sitbon

Science and Engineering Faculty, Queensland University of Technology, Brisbane 4000, Queensland, Australia.
Guido Zuccon

Queensland University of Technology, Brisbane, QLD, Australia.
Anthony Nguyen

Australian e-Health Research Centre, CSIRO, Brisbane, QLD, Australia.

Keywords

Algorithms Electronic Health Records Humans Information Storage and Retrieval Machine Learning Natural Language Processing Problem-Based Learning

External Resources

View on PubMed Access via DOI PubMed (28870380)

Active learning reduces annotation time for clinical concept extraction.

Abstract

Authors

Keywords

External Resources

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