Syapse at the NTCIR-16 RealMed-NLP task

In this paper, we present our approach to subtasks 1, 2, and 3 of the NTCIR-16 RealMed-NLP challenge. For these challenges, the english language corpora (CR-EN and RR-EN) were used. In subtasks 1 and 2, the goal was to create an NLP system which could add tags to case reports (CR) or radiology reports (RR). In subtask 3, two applications of this system were tested: the ability to determine which RRs from a group referred to the same sample, and the ability to determine the probability that a medication caused side effects in a report. Our approach leveraged keyword extraction through a medical metathesaurus (MetaMap), sentence structuring using a SciSpacy model, and word embeddings using a trained BERT model. Using this model, we were able to complete these three subtasks with high levels of accuracy.