Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs.

ABSTRACT: Real-world data (RWD) in the medical field, such as electronic health records (EHRs) and medication orders, are receiving increasing attention from researchers and practitioners. While structured data have played a vital role thus far, unstructured data represented by text (e.g., discharge summaries) are not effectively utilized because of the difficulty in extracting medical information. We evaluated the information gained by supplementing structured data with clinical concepts extracted from unstructured text by leveraging natural language processing techniques. Using a machine learning-based pretrained named entity recognition tool, we extracted disease and medication names from real discharge summaries in a Japanese hospital and linked them to medical concepts using medical term dictio

SUBMITTER: Yada S 

PROVIDER: S-EPMC11389901 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature