We present an open-access natural language processing toolkit for Japanese medical information extraction. We first propose a novel relation annotation schema for investigating the medical and temporal relations between medical entities in Japanese medical reports. We experiment with the practical annotation scenarios by separately annotating two different types of reports. We design a pipeline system with three components for recognizing medical entities, classifying entity modalities, and extracting relations. The empirical results show accurate analyzing performance and suggest the satisfactory annotation quality, the effective annotation strategy for targeting report types, and the superiority of the latest contextual embedding models.
@article{arxiv.2111.04261,
title = {JaMIE: A Pipeline Japanese Medical Information Extraction System},
author = {Fei Cheng and Shuntaro Yada and Ribeka Tanaka and Eiji Aramaki and Sadao Kurohashi},
journal= {arXiv preprint arXiv:2111.04261},
year = {2021}
}