id: 05559136
dt: a
an: 05559136
au: Kouznetsov, Alexandre; Matwin, Stan; Inkpen, Diana; Razavi, Amir H.;
    Frunza, Oana; Sehatkar, Morvarid; Seaward, Leanne; O’Blenis, Peter
ti: Classifying biomedical abstracts using committees of classifiers and
    collective ranking techniques.
so: Gao, Yong (ed.) et al., Advances in artificial intelligence. 22nd Canadian
    conference on artificial intelligence, Canadian AI 2009, Kelowna,
    Canada, May 25‒27, 2009. Proceedings. Berlin: Springer (ISBN
    978-3-642-01817-6/pbk). Lecture Notes in Computer Science 5549. Lecture
    Notes in Artificial Intelligence, 224-228 (2009).
py: 2009
pu: Berlin: Springer
la: EN
cc: 
ut: Machine Learning; Automatic Text Classification; Systematic Reviews;
    Ranking Algorithms
ci: 
li: doi:10.1007/978-3-642-01818-3_29
ab: Summary: The purpose of this work is to reduce the workload of human
    experts in building systematic reviews from published articles, used in
    evidence-based medicine. We propose to use a committee of classifiers
    to rank biomedical abstracts based on the predicted relevance to the
    topic under review. In our approach, we identify two subsets of
    abstracts: one that represents the top, and another that represents the
    bottom of the ranked list. These subsets, identified using machine
    learning (ML) techniques, are considered zones where abstracts are
    labeled with high confidence as relevant or irrelevant to the topic of
    the review. Early experiments with this approach using different
    classifiers and different representation techniques show significant
    workload reduction.
rv: