A pilot study using machine learning and domain knowledge to facilitate comparative effectiveness review updating.

Background: Comparative effectiveness and systematic reviews require frequent and time-consuming updating.

Results: of earlier screening should be useful in reducing the effort needed to screen relevant articles.

Methods: We collected 16,707 PubMed citation classification decisions from 2 comparative effectiveness reviews: interventions to prevent fractures in low bone density (LBD) and off-label uses of atypical antipsychotic drugs (AAP).

Using information mining of the medical literature to improve drug safety.

Objective: Prescription drugs can be associated with adverse effects (AEs) that are unrecognized despite evidence in the medical literature, as shown by rofecoxib's late recall in 2004. We assessed whether applying information mining to PubMed could reveal major drug-AE associations if articles testing whether drugs cause AEs are over-represented in the literature.

Design: MEDLINE citations published between 1949 and September 2009 were retrieved if they mentioned one of 38 drugs and one of 55 AEs.