What question did this study set out to answer?

The study aims to develop a natural language processing system for tagging medical reports and analyzing relationships related to medications.

April 1, 2026Open Access

Syapse at the NTCIR-16 RealMed-NLP task

Key Points

The study aims to develop a natural language processing system for tagging medical reports and analyzing relationships related to medications.
Used English language corpora CR-EN and RR-EN for subtasks
Implemented keyword extraction utilizing a medical metathesaurus (MetaMap)
Employed a SciSpacy model for sentence structuring
Applied a BERT model for word embeddings to enhance accuracy
Achieved high accuracy in tagging case reports and radiology reports
Successfully identified reports referring to the same sample
Determined the probability of medication causing side effects with high accuracy

Abstract

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.

Read Full Paperexternally

AIに質問

Bookmark

View Full Paper