Cover: A Pilot Study Using Machine Learning and Domain Knowledge to Facilitate Comparative Effectiveness Review Updating

A Pilot Study Using Machine Learning and Domain Knowledge to Facilitate Comparative Effectiveness Review Updating

Published in: Medical Decision Making, v. 33, no. 3, Apr. 2013, p. 343-355

Posted on Sep 1, 2012

by Siddhartha Dalal, Paul G. Shekelle, Susanne Hempel, Sydne J. Newberry, Aneesa Motala, Kanaka Shetty

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). We used previously written search strategies to guide extraction of a limited number of explanatory variables pertaining to the intervention, outcome, and study design. We empirically derived statistical models (based on a sparse generalized linear model with convex penalties [GLMnet] and a gradient boosting machine [GBM]) that predicted article relevance. We evaluated model sensitivity, positive predictive value (PPV), and screening workload reductions using 11,003 PubMed citations retrieved for the LBD and AAP updates. RESULTS: GLMnet-based models performed slightly better than GBM-based models. When attempting to maximize sensitivity for all relevant articles, GLMnet-based models achieved high sensitivities (0.99 and 1.0 for AAP and LBD, respectively) while reducing projected screening by 55.4% and 63.2%. The GLMnet-based model yielded sensitivities of 0.921 and 0.905 and PPVs of 0.185 and 0.102 when predicting articles relevant to the AAP and LBD efficacy/effectiveness analyses, respectively (using a threshold of P ≥ 0.02). GLMnet performed better when identifying adverse effect relevant articles for the AAP review (sensitivity = 0.981) than for the LBD review (0.685). The system currently requires MEDLINE-indexed articles. CONCLUSIONS: We evaluated statistical classifiers that used previous classification decisions and explanatory variables derived from MEDLINE indexing terms to predict inclusion decisions. This pilot system reduced workload associated with screening 2 simulated comparative effectiveness review updates by more than 50% with minimal loss of relevant articles.

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