title: Information extraction for tracking liver cancer patients' statuses: from mixture of clinical narrative report types.
abstract: OBJECTIVE
  To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports.
  
  
  METHODS
  To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and the classifier, the gold-standard annotations and answers were developed manually, and the results of the implemented system were compared with the gold standard.
  
  
  RESULTS
  The IE model achieved an F-score from 92.40% to 99.59%, and the classifier achieved accuracy from 96.15% to 100%.
  
  
  CONCLUSIONS
  The application was successfully applied to the various types of narrative clinical reports. It might be applied to the key extraction for other types of cancer patients.
authors.affiliation: 1 Department of Computer Science and Information Engineering, National Taiwan University , Taipei, Taiwan .
authors.forename: Xiao-Ou
authors.initials: XO
authors.lastname: Ping
authors.forename: Yi-Ju
authors.initials: YJ
authors.lastname: Tseng
authors.forename: Yufang
authors.initials: Y
authors.lastname: Chung
authors.forename: Ya-Lin
authors.initials: YL
authors.lastname: Wu
authors.forename: Ching-Wei
authors.initials: CW
authors.lastname: Hsu
authors.forename: Pei-Ming
authors.initials: PM
authors.lastname: Yang
authors.forename: Guan-Tarn
authors.initials: GT
authors.lastname: Huang
authors.forename: Feipei
authors.initials: F
authors.lastname: Lai
authors.forename: Ja-Der
authors.initials: JD
authors.lastname: Liang
mesh_terms: D057225:Data Mining / Q000379:methods*; D018450:Disease Progression; D057286:Electronic Health Records; D005260:Female; D006304:Health Status; D006801:Humans; D008113:Liver Neoplasms; D008297:Male; D008962:Models, Theoretical; D009323:Natural Language Processing; D013624:Taiwan