Normalization of Medical Concepts in Clinical Narrative

Normalization maps clinical terms in medical notes to concepts in standardized medical vocabularies. To complement the traditional lexical transformation based approach, we propose a hybrid normalization system which incorporates a deep learning model to capture semantic similarity between different surface expressions of the same concept. When evaluating our system against the mentions which may be normalized to existing concepts in the ShARe/CLEF eHealth 2013 dataset, our hybrid system achieves 90.6% in accuracy and outperforms a strong exact match + edit distance baseline by 2.6%. The results suggest the potential of the deep learning model to further improve the performance of normalization by mapping concept mentions to concepts using semantic similarity.


Y. Luo W. Sun A. Rumshisky A Hybrid Method for Normalization of Medical Concepts in Clinical Narrative. International Conference on Health Informatics and Health Information Technology. 2018

@inproceedings{luo2018hybrid, title={A Hybrid Method for Normalization of Medical Concepts in Clinical Narrative}, author={Luo, Yen-Fu and Sun, Weiyi and Rumshisky, Anna}, booktitle={2018 IEEE International Conference on Healthcare Informatics (ICHI)}, pages={392--393}, year={2018}, organization={IEEE} }