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KDD
2010
ACM

Active learning for biomedical citation screening

13 years 5 months ago
Active learning for biomedical citation screening
Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe a real-world, deployed application of AL to the problem of biomedical citation screening for systematic reviews at the Tufts Evidence-based Practice Center. We propose a novel active learning strategy that exploits a priori domain knowledge provided by the expert (specifically, labeled features) and extend this model via a Linear Programming algorithm for situations where the expert can provide ranked labeled features. Our methods outperform existing AL strategies on three real-world systematic review datasets. We argue that evaluation must be specific to the scenario under consideration. To this end, we propose a new evaluation framework for finite-pool scenarios, wherein the primary aim is to label a fixed set of examples rather than to simply induce a good predictive model. We use a method from medical...
Byron C. Wallace, Kevin Small, Carla E. Brodley, T
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2010
Where KDD
Authors Byron C. Wallace, Kevin Small, Carla E. Brodley, Thomas A. Trikalinos
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