Using discharge summaries to improve information retrieval in clinical domain

Dongqing Zhu, Wu Stephen, Masanz James, Ben Carterette, Hongfang Liu

Research output: Contribution to journalConference article

Abstract

Task 3 of the 2013 ShARe/CLEF eHealth Evaluation Lab simulated web searches for health information by patients. The web searches were designed to be connected to hospital discharge summaries from the patient's Electronic Medical Record (EMR), thus effectually modeling a post-visit information need. We primarily investigated three research questions about the retrieval of medical information from the web: 1) to what degree retrieval techniques effective in searching Electronic Medical Records (EMRs) could aid in finding medical web documents; 2) to what degree medical web retrieval would benefit from natural language processing (NLP) techniques that extract information from text based on medical knowledge; and 3) how to leverage contextual information in the patient's discharge summaries to improve retrieval. We submitted seven runs to ShARe/CLEF eHealth. Our best run used effective EMR-based IR techniques, NLP-produced information, and information in patients' discharge summaries to achieve precision at 10 (P@10) scores at or above the CLEF median for all but 2 of 50 test queries.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume1179
StatePublished - Jan 1 2013
Event2013 Cross Language Evaluation Forum Conference, CLEF 2013 - Valencia, Spain
Duration: Sep 23 2013Sep 26 2013

Keywords

  • Language models
  • Markov Random Field
  • MeSH
  • Mixture of relevance models
  • Semantic concepts
  • UMLS Metathesaurus

ASJC Scopus subject areas

  • Computer Science(all)

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