JaMIE: A Pipeline Japanese Medical Information Extraction System

TL;DR

This paper introduces JaMIE, an open-source NLP toolkit for extracting medical information from Japanese reports, featuring a new relation schema, a three-component pipeline, and effective use of contextual embeddings.

Contribution

It presents a novel relation annotation schema and a pipeline system tailored for Japanese medical reports, demonstrating high accuracy and annotation quality.

Findings

Accurate relation extraction performance

Effective annotation strategy for different report types

Superiority of latest contextual embedding models

Abstract

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.