Information extraction for tracking liver cancer patients' statuses: From mixture of clinical narrative report types

Xiao Ou Ping, Yi Ju Tseng, Yufang Chung, Ya Lin Wu, Ching Wei Hsu, Pei Ming Yang, Guan Tarn Huang, Feipei Lai, Ja Der Liang

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

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. Materials and 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.

Original languageEnglish
Pages (from-to)704-710
Number of pages7
JournalTelemedicine and e-Health
Volume19
Issue number9
DOIs
StatePublished - 1 Sep 2013

Keywords

  • e-health
  • information management
  • medical records
  • technology

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