An improved data augmentation approach and its application in medical named entity recognition

• Published in: BMC medical informatics and decision making Vol. 24; no. 1; pp. 221 - 13
• Main Authors: Chen, Hongyu, Dan, Li, Lu, Yonghe, Chen, Minghong, Zhang, Jinxia
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 05.08.2024; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Annotations; Classification; Computational linguistics; Data augmentation; Data mining; Deep Learning; Electronic health records; Evaluation; Humans; Language; Language processing; Machine learning; Medical named entity recognition; Medical records; Methods; Natural language interfaces; Natural Language Processing; Neural networks; Recognition; Replacement augmentation; Semantics; Terminology; Text categorization; Text features; Training; China; Deep learning; Data augmentation; Text features; Replacement augmentation; Medical named entity recognition

Performing data augmentation in medical named entity recognition (NER) is crucial due to the unique challenges posed by this field. Medical data is characterized by high acquisition costs, specialized terminology, imbalanced distributions, and limited training resources. These factors make achieving...