Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts (2020)

Abstract: Medical concept normalization (MCN) i.e., mapping of colloquial medical phrases to standard concepts is an essential step in analysis of medical social media text. The main drawback in existing state-of-the-art approach (Kalyan and Sangeetha, 2020b) is learning target concept vector representations from scratch which requires more number of training instances. Our model is based on RoBERTa and target concept embeddings. In our model, we integrate a) target concept information in the form of target concept vectors generated by encoding target concept descriptions using SRoBERTa, state-of-the-art RoBERTa based sentence embedding model and b) domain lexicon knowledge by enriching target concept vectors with synonym relationship knowledge using retrofitting algorithm. It is the first attempt in MCN to exploit both target concept information as well as domain lexicon knowledge in the form of retrofitted target concept vectors. Our model outperforms all the existing models with an accuracy improvement up to 1.36% on three standard datasets. Further, our model when trained only on mapping lexicon synonyms achieves up to 4.87% improvement in accuracy.

## introduction medical concept normalization (mcn) involves learning a model which can assign medical concept from a standard lexicon for the given health related mention. ## datasets our proposed model is evaluated on three standard mcn datasets of noisy user-generated texts. out of these, cadec @xcite and psytar @xcite datasets contain concept mentions gathered from user-generated askapatient.com reviews and smm4h2017 @xcite ) contains adverse drug reaction (adr) mentions extracted from twitter. cadec: karimi et.al released csiro adverse drug event corpus (cadec) having user posted drug reviews gathered from askapatient @xcite . the annotators manually identified concept mentions and mapped them to snomed-ct concepts which resulted in a corpus of 6754 concept mentions and 1029 snomed-ct codes. as 66% of instances are common in train and test splits in the random folds of this dataset released by @xcite collier (2016 the significant overlap between train and test sets in random folds of cadec and psytar datasets can result in bias and contribute to high performance of model @xcite . so, we evaluate our approach on custom folds of psytar and cadec datasets in addition to smm4h2017 dataset, like the recent previous works @xcite 4 methodology ## results our proposed model is evaluated on the standard mcn datasets cadec, psytar and smm4h2017. the performance of our model and existing models is presented in