Incremental Abbreviation Detection in Clinical Texts

摘要

In this paper, incremental abbreviation detection in clinical texts is considered for the practical context where new clinical texts are added, processed, and exploited over time. We propose a parameter-free semi-supervised method, named nonThreshold-ST, based on Self-training and C4.5. It inherits the prediction capability and simplicity of Self-training, while exploiting additional instances that are the most confidently predicted for enhancing the training dataset in the parameter-free configuration manner. The experimental results show that it outperforms its base supervised learning method on different English clinical text sets. Moreover, its Recall, Precision, F-measure, and Accuracy are among the highest values as compared to those of some other semi-supervised learning methods.