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 42.8% respectively. This rule-based module will help health care personnel reduce the cost of manual tagging correction for corpus building.
Nishimoto
Naoki
N
Department of Medical Informatics, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
Terae
Satoshi
S
Uesugi
Masahito
M
Tanikawa
Takumi
T
Endou
Akira
A
Endoh
Akira
A
Ogasawara
Katsuhiko
K
Sakurai
Tsunetaro
T
D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods; D007564:Japan; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011873:Radiology Information Systems / Q000706:statistics & numerical data*; D012660:Semantics; D009626:Terminology as Topic