Optimizing feature representation for automated systematic review work prioritization

AMIA Annu Symp Proc. 2008 Nov 6;2008:121-5.

Abstract

Automated document classification can be a valuable tool for enhancing the efficiency of creating and updating systematic reviews (SRs) for evidence-based medicine. One way document classification can help is in performing work prioritization: given a set of documents, order them such that the most likely useful documents appear first. We evaluated several alternate classification feature systems including unigram, n-gram, MeSH, and natural language processing (NLP) feature sets for their usefulness on 15 SR tasks, using the area under the receiver operating curve as a measure of goodness. We also examined the impact of topic-specific training data compared to general SR inclusion data. The best feature set used a combination of n-gram and MeSH features. NLP-based features were not found to improve performance. Furthermore, topic-specific training data usually provides a significant performance gain over more general SR training.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Abstracting and Indexing / methods*
  • Artificial Intelligence*
  • Documentation / classification*
  • Documentation / methods*
  • Evidence-Based Medicine*
  • Health Priorities*
  • Natural Language Processing*
  • Oregon
  • Pattern Recognition, Automated / methods
  • Workload