Top-Level MeSH Disease Terms Are Not Linearly Separable in Clinical Trial Abstracts
Assessments of the efficacy and safety of medical interventions are based on systematic reviews of clinical trials. Systematic reviewing requires the screening of vast amounts of publications, which is currently done by hand. To reduce the number of publications that are screened manually, we propose the automated classification of publications by disease category using Support Vector Machines. We base our classification on the ontological structure of the (MeSH) by treating all terms as their top-level disease category. Unfortunately the resulting classifier lacks sufficient sensitivity for use by systematic reviewers. We argue that this is partially due to the inseparability of the terminology into the disease categories and discuss how future work could address this problem.
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