title: PICO element detection in medical text without metadata: are first sentences enough?
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<0.001) and F-measures (0.728±0.006 vs. 0.662±0.007, p<0.001). For P-elements, CF have higher precision (0.714±0.009 vs. 0.665±0.010, p<0.001), but lower recall (0.766±0.013 vs. 0.811±0.012, p<0.001). CF are not always better than CA in sentence-level PICO element detection. Their performance varies in detecting different elements.
authors.affiliation: Institute of Biomedical Engineering, National Taiwan University, Taipei 10051, Taiwan.
authors.forename: Ke-Chun
authors.initials: KC
authors.lastname: Huang
authors.forename: I-Jen
authors.initials: IJ
authors.lastname: Chiang
authors.forename: Furen
authors.initials: F
authors.lastname: Xiao
authors.forename: Chun-Chih
authors.initials: CC
authors.lastname: Liao
authors.forename: Charles Chih-Ho
authors.initials: CC
authors.lastname: Liu
authors.forename: Jau-Min
authors.initials: JM
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D009323:Natural Language Processing
keywords: Evidence-based medicine; Information extraction; Information retrieval; Natural language processing; Question answering; Text mining