Parsing error correction of medical phrases for semantic annotation of clinical radiology reports

The purpose of this study is to develop a module for correcting errors in the product of a natural language parser. When tested with 300 CT reports, a total of 604 patterns were generated. The recall and precision was improved to 90.7% and 74.1% after processed by the module from initial 80.5% and 4...

Full description

Saved in:
Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2008-11, p.1070-1070
Main Authors: Nishimoto, Naoki, Terae, Satoshi, Uesugi, Masahito, Tanikawa, Takumi, Endou, Akira, Endoh, Akira, Ogasawara, Katsuhiko, Sakurai, Tsunetaro
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Information Storage and Retrieval - methods
Japan
Medical Records Systems, Computerized - statistics & numerical data
Natural Language Processing
Pattern Recognition, Automated - methods
Radiology Information Systems - statistics & numerical data
Semantics
Terminology as Topic
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The purpose of this study is to develop a module for correcting errors in the product of a natural language parser. When tested with 300 CT reports, a total of 604 patterns were generated. The recall and precision was improved to 90.7% and 74.1% after processed by the module from initial 80.5% and 42.8% respectively. This rule-based module will help health care personnel reduce the cost of manual tagging correction for corpus building.
ISSN:1559-4076