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

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 with our information model. RESULTS: The microaveraged F(1)-scores of our best-performing model for entity extraction and relation extraction were 96.1% and 97.4%, respectively. The microaveraged F(1)-score of the 2-stage system, which is a measure of the performance of the entire pipeline of our system, was 91.9%. Our system showed encouraging results for the conversion of free-text radiology reports into a structured format. The coverage of clinical information in the reports was 96.2% (6595/6853). CONCLUSIONS: Our 2-stage deep system can extract clinical information from chest and abdomen CT reports accurately and comprehensively.