title: Development of a medical-text parsing algorithm based on character adjacent probability distribution for Japanese radiology reports.
abstract: OBJECTIVE
  The objectives of this study were to investigate the transitional probability distribution of medical term boundaries between characters and to develop a parsing algorithm specifically for medical texts.
  
  
  METHODS
  Medical terms in Japanese computed tomography (CT) reports were identified using the ChaSen morphological analysis system. MeSH-based medical terms (51,385 entries), obtained from the metathesaurus in the Unified Medical Language System (UMLS, 2005AA), were added as a medical dictionary for ChaSen. A radiographer corrected the set of results containing 300 parsed CT reports. In addition, two radiologists checked the medical term parsing of 200 CT sentences.
  
  
  RESULTS
  We obtained modified inter-annotator agreement scores for the text corrected by the radiologists. We retrieved the transitional probability as the conditional probability of a uni-gram, bi-gram, and tri-gram. The highest transitional probability P(Ci | Ci- 2(*)Ci- 1) was 1.00. For an example of anatomical location, the term "pulmonary hilum" was parsed as a tri-gram.
  
  
  CONCLUSIONS
  Retrieval of transitional probability will improve the accuracy of parsing compound medical terms.
authors.affiliation: Department of Medical Informatics, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan. nishimot@med.hokudai.ac.jp
authors.forename: N
authors.initials: N
authors.lastname: Nishimoto
authors.forename: S
authors.initials: S
authors.lastname: Terae
authors.forename: M
authors.initials: M
authors.lastname: Uesugi
authors.forename: K
authors.initials: K
authors.lastname: Ogasawara
authors.forename: T
authors.initials: T
authors.lastname: Sakurai
mesh_terms: D022126:Access to Information; D000465:Algorithms; D006801:Humans; D007564:Japan; D008390:Markov Chains; D008490:Medical Informatics / Q000379:methods / Q000458:organization & administration*; D015233:Models, Statistical; D008962:Models, Theoretical; D009323:Natural Language Processing; D011336:Probability; D011871:Radiology / Q000379:methods* / Q000458:organization & administration; D009626:Terminology as Topic; D014057:Tomography, X-Ray Computed; D017432:Unified Medical Language System