SYRIAC: The systematic review information automated collection system a data warehouse for facilitating automated biomedical text classification

AMIA Annu Symp Proc. 2008 Nov 6:2008:825-9.

Abstract

Automatic document classification can be valuable in increasing the efficiency in updating systematic reviews (SR). In order for the machine learning process to work well, it is critical to create and maintain high-quality training datasets consisting of expert SR inclusion/exclusion decisions. This task can be laborious, especially when the number of topics is large and source data format is inconsistent.To approach this problem, we build an automated system to streamline the required steps, from initial notification of update in source annotation files to loading the data warehouse, along with a web interface to monitor the status of each topic. In our current collection of 26 SR topics, we were able to standardize almost all of the relevance judgments and recovered PMIDs for over 80% of all articles. Of those PMIDs, over 99% were correct in a manual random sample study. Our system performs an essential function in creating training and evaluation data sets for SR text mining research.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Abstracting and Indexing* / methods
  • Algorithms
  • Artificial Intelligence
  • Databases, Factual*
  • Natural Language Processing*
  • Pattern Recognition, Automated* / methods
  • Periodicals as Topic* / classification
  • PubMed* / classification
  • Systematic Reviews as Topic
  • Terminology as Topic*
  • United States