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Updated: Jul 5, 2025

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
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Towards Structuring Clinical Texts: Joint Entity and Relation Extraction from Japanese Case Report Corpus
Daisaku Shibata1, Emiko Shinohara1, Kiminori Shimamoto1
1Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Studies in Health Technology and Informatics
|January 25, 2024
Summary
This study demonstrates high accuracy in extracting patient symptom and diagnosis information from clinical texts using natural language processing (NLP). The machine learning models achieved excellent performance in named entity recognition (NER) and relation extraction (RE).
Area of Science:
- Clinical Informatics
- Natural Language Processing
- Machine Learning
Background:
- Clinical texts contain crucial patient symptom and diagnosis data in free-text format.
- Extracting this information requires advanced Natural Language Processing (NLP) techniques.
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