title: Parsing error correction of medical phrases for semantic annotation of clinical radiology reports.
abstract: 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.
authors.affiliation: Department of Medical Informatics, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
authors.forename: Naoki
authors.initials: N
authors.lastname: Nishimoto
authors.forename: Satoshi
authors.initials: S
authors.lastname: Terae
authors.forename: Masahito
authors.initials: M
authors.lastname: Uesugi
authors.forename: Takumi
authors.initials: T
authors.lastname: Tanikawa
authors.forename: Akira
authors.initials: A
authors.lastname: Endou
authors.forename: Akira
authors.initials: A
authors.lastname: Endoh
authors.forename: Katsuhiko
authors.initials: K
authors.lastname: Ogasawara
authors.forename: Tsunetaro
authors.initials: T
authors.lastname: Sakurai
mesh_terms: 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