Abstract
Systematic reviews are considered fundamental tools for Evidence-Based Medicine. Such reviews require frequent and time- consuming updating. This study aims to compare the performance of combining relatively simple Bayesian classifiers using a fixed rule, to the relatively complex linear Support Vector Machine for medical systematic reviews. A collection of four systematic drug reviews is used to compare the performance of the classifiers in this study. Cross-validation experiments were performed to evaluate performance. We found that combining Discriminative Multinomial Naïve Bayes and Complement Naïve Bayes performs equally well or better than SVM while being about 25% faster than SVM in training time. The results support the usefulness of using an ensemble of Bayesian classifiers for machine learning-based automation of systematic reviews of medical topics, especially when datasets have a large number of abstracts. Further work is needed to integrate the powerful features of such Bayesian classifiers together.
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Aref, A., Tran, T. (2014). Using Ensemble of Bayesian Classifying Algorithms for Medical Systematic Reviews. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_23
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DOI: https://doi.org/10.1007/978-3-319-06483-3_23
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