Information Extraction Using Concept Node Analysis of Brain Radiology Reports Summarization.
- Author:
Miyoung KWAK
1
;
Jinwook CHOI
Author Information
1. Department of Biomedical Engineering, College of Medicine, Seoul National University, Korea. jinchoi@snu.ac.kr
- Publication Type:Original Article
- Keywords:
Information Extraction;
Concept Node Analysis;
Radiology Report;
Conceptual Model;
Document Summarization
- MeSH:
Brain*;
Electronic Health Records;
Hope;
Information Storage and Retrieval*;
Medical Records;
Semantics
- From:Journal of Korean Society of Medical Informatics
2005;11(1):57-70
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
OBJECTIVE: Electronic Medical Record contains the majority of clinical data in unstructured text. The information in the textual document can be stored in conceptual format and used to support clinical care by text summarization technique. In this study, we present Information Extraction(IE) using Concept Node(CN) which is extraction rule in case frame from brain radiology reports in SNUH(Seoul National University Hospital) for summarization. METHOD: Following steps are performed: design conceptual model to define semantic entities as extraction templates of brain radiology report, build CN dictionary based on statistical syntactic pattern and development of parser to extract relevant information based on defined templates. RESULTS: The three evaluation results shows that 19% precision improvement after post processing supplemental specified complex verb construction and 19.24~21.25% accurate semantic effectiveness with extracting additional Korean noun. The average of precision is 85.18%, average of recall is 93.71% and F-measure is 0.89. CONCLUSION: Our approach has advantageous elements for different language at the same sentence. We expect this IE technology can summarize vast amount radiology texts material for clinical decision support system effectively and hope this study helps the evolution of clinical data representation in Korean medical records and its integration into the EMR in the future.
