Retrieving Medical Records with sennamed: NEC Labs America at TREC 2012 Medical Records Track

Abstract

In this notebook, we describe the automatic retrieval runs from NEC Laboratories America (NECLA) for the Text REtrieval Conference (TREC) 2012 Medical Records track. Our approach is based on a combination of UMLS medical concept detection and a set of simple retrieval models. Our best run, sennamed2, has achieved the best inferred average precision (infAP) score on 5 of the 47 test topics, and obtained a higher score than the median of all submission runs on 27 other topics. Overall, sennamed2 ranks at the second place amongst all the 82 automatic runs submitted for this track, and obtains the third place amongst both automatic and manual submissions.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2012
Accession Number
ADA581305

Entities

People

  • Pierre-francois Laquerre
  • Yanjun Qi

Organizations

  • NEC Laboratories America

Tags

DTIC Thesaurus Topics

  • Automatic
  • Detection
  • Dimensionality Reduction
  • Feedback
  • Health Services
  • Hematologic Diseases
  • Information Retrieval
  • Language
  • Learning
  • Machine Learning
  • Medical Personnel
  • Natural Language Processing
  • Natural Languages
  • Pain
  • Standards
  • Test And Evaluation
  • Vector Spaces

Readers

  • Information Retrieval

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval