Abstract

This paper will explore methods for effectively extracting information from clinical narratives. The proposed research will investigate the application of state of the art natural language processing techniques to clinical narratives, such as medical admission notes, discharge summaries, progress notes, and pathology reports to extract information of interest. The objective of this knowledge discovery task is the ability to generate a chronology of events, for a given patient, ultimately leading to patient cohort discovery. This in turn facilitates efficient information retrieval and enables patient specific question answering. The paper details the proposed research problem which spans across areas of information extraction, temporal relation extraction and reasoning, and machine learning with the help of two use case scenarios: 1) Automatic patient accrual for clinical trials and 2) Information retrieval over a biorepository.

Original languageEnglish
Title of host publicationProceedings of the 3rd Workshop on Ph.D. Students in Information and Knowledge Management, PIKM'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10
Pages57-65
Number of pages9
DOIs
StatePublished - 2010
Event3rd Workshop on Ph.D. Students in Information and Knowledge Management, PIKM'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10 - Toronto, ON, Canada
Duration: Oct 26 2010Oct 30 2010

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference3rd Workshop on Ph.D. Students in Information and Knowledge Management, PIKM'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10
Country/TerritoryCanada
CityToronto, ON
Period10/26/1010/30/10

Keywords

  • Clinical narratives
  • Coreference resolution
  • Information extraction
  • Phenotype modeling
  • Temporal reasoning

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