Screening PubMed abstracts: is class imbalance always a challenge to machine learning?
Author:
Publisher
Springer Science and Business Media LLC
Subject
Medicine (miscellaneous)
Link
http://link.springer.com/content/pdf/10.1186/s13643-019-1245-8.pdf
Reference28 articles.
1. Thomas J, Noel-Storr A, Marshall I, et al. Living systematic reviews: 2. Combining human and machine effort. J Clin Epidemiol. 2017;91:31–7.
2. Khabsa M, Elmagarmid A, Ilyas I, et al. Learning to identify relevant studies for systematic reviews using random forest and external information. Mach Learn. 2016;102:465–82.
3. Marshall IJ, Noel-Storr A, Kuiper J, et al. Machine learning for identifying randomized controlled trials: an evaluation and practitioner’s guide. Res Synth Methods:0. Epub ahead of print January 2018. https://doi.org/10.1002/jrsm.1287 .
4. Wallace BC, Noel-Storr A, Marshall IJ, et al. Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach. J Am Med Inform Assoc. 2017;24:1165–8.
5. Miwa M, Thomas J, O’Mara-Eves A, et al. Reducing systematic review workload through certainty-based screening. J Biomed Inform. 2014;51:242–53.
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