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

Abstract

Abstract BackgroundRadiology reports are usually written in a free-text format, which makes it challenging to reuse the reports. ObjectiveFor secondary use, we developed a 2-stage deep learning system for extracting clinical information and converting it into a structured format. MethodsOur 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 with our information model. ResultsThe microaveraged F1F1 ConclusionsOur 2-stage deep system can extract clinical information from chest and abdomen CT reports accurately and comprehensively.