Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and target effectiveness.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Nov 1, 2024

Abstract

OBJECTIVES: Active learning (AL) has rarely integrated diversity-based and uncertainty-based strategies into a dynamic sampling framework for clinical named entity recognition (NER). Machine-assisted annotation is becoming popular for creating gold-standard labels. This study investigated the effectiveness of dynamic AL strategies under simulated machine-assisted annotation scenarios for clinical NER.

Authors

Jiaxing Liu

School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China.
Zoie S Y Wong

Graduate School of Public Health, St. Luke's International University, Tokyo, 104-0045, Japan. Electronic address: zoiesywong@gmail.com.

Keywords

Datasets as Topic Electronic Health Records Humans Natural Language Processing Supervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (39081233)

Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and target effectiveness.

Abstract

Authors

Keywords

External Resources

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