PICO element detection in medical text without metadata: Are first sentences enough?

Ke Chun Huang, I-Jen Chiang, Furen Xiao, Chun Chih Liao, Charles Chih Ho Liu, Jau Min Wong

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Efficient identification of patient, intervention, comparison, and outcome (PICO) components in medical articles is helpful in evidence-based medicine. The purpose of this study is to clarify whether first sentences of these components are good enough to train naive Bayes classifiers for sentence-level PICO element detection. We extracted 19,854 structured abstracts of randomized controlled trials with any P/I/O label from PubMed for naive Bayes classifiers training. Performances of classifiers trained by first sentences of each section ( CF) and those trained by all sentences ( CA) were compared using all sentences by ten-fold cross-validation. The results measured by recall, precision, and F-measures show that there are no significant differences in performance between CF and CA for detection of O-element ( F-measure. = 0.731. ±. 0.009 vs. 0.738. ±. 0.010, p= 0.123). However, CA perform better for I-elements, in terms of recall (0.752. ±. 0.012 vs. 0.620. ±. 0.007, p

Original languageEnglish
Pages (from-to)940-946
Number of pages7
JournalJournal of Biomedical Informatics
Volume46
Issue number5
DOIs
Publication statusPublished - Oct 2013

Keywords

  • Evidence-based medicine
  • Information extraction
  • Information retrieval
  • Natural language processing
  • Question answering
  • Text mining

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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  • Cite this

    Huang, K. C., Chiang, I-J., Xiao, F., Liao, C. C., Liu, C. C. H., & Wong, J. M. (2013). PICO element detection in medical text without metadata: Are first sentences enough? Journal of Biomedical Informatics, 46(5), 940-946. https://doi.org/10.1016/j.jbi.2013.07.009