Medical terminology-based computing system: a lightweight post-processing solution for out-of-vocabulary multi-word terms - Saeed , Nadia; Naveed, Hammad (Frontiers in Molecular Biosciences, 2022)
The linguistic rules of medical terminology assist in gaining acquaintance with rare/complex clinical and biomedical terms. Medical language follows a Greek and Latin inspired nomenclature. This nomenclature aids the stakeholders in simplifying the medical terms and gaining semantic familiarity. However, natural language processing models misrepresent rare and complex biomedical words. In this paper, we present MedTCS - a lightweight, post-processing module to simplify hybridized or compound terms into regular words using medical nomenclature. MedTCS enabled the word-based embedding models to achieve 100% coverage and enabled the BiowordVec model to achieve high correlation-scores (0.641 and 0.603 in UMNSRS similarity and relatedness datasets respectively) that significantly surpass the n-gram and sub-word approaches of FastText and BERT. In the downstream task of named entity recognition (NER), the MedTCS enabled the latest clinical embedding model of FastText-OA-All-300d to improve the F1-score from 0.45 to 0.80 on the BC5CDR corpus and from 0.59 to 0.81 on the NCBI-Disease corpus respectively. Similarly, in the drug indication classification task our model was able to increase the coverage by 9% and the F1-score by 1%. Our results indicate that incorporating a medical terminology-based module provides distinctive contextual clues to enhance vocabulary as a post processing step on pre-trained embeddings. We demonstrate that the proposed module enabled the word embedding models to generate vectors of out-of-vocabulary words effectively. We expect that our study can be a stepping stone for use of biomedical knowledge-driven resources in NLP.