Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation.

ABSTRACT:

Background

Radiology reports are usually written in a free-text format, which makes it challenging to reuse the reports.

Objective

For secondary use, we developed a 2-stage deep learning system for extracting clinical information and converting it into a structured format.

Methods

Our system mainly consists of 2 deep learning modules: entity extraction and relation extraction. For each module, state-of-the-art deep learning models were applied. We trained and evaluated the models using 1040 in-house Japanese computed tomography (CT) reports annotated by medical experts. We also evaluated the performance of the entire pipeline of our system. In addition, the ratio of annotated entities in the reports was measured to validate the coverage of the clinical information wit

SUBMITTER: Sugimoto K 

PROVIDER: S-EPMC10686535 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature