Improving Biomedical Information Retrieval Citation Metrics Using Machine Learning

Abstract

The evaluation of the literature is an increasingly integral part of biomedical research. Clinicians, researchers, librarians, and others routinely use the literature to answer questions for clinical care and research. The size of the literature prevents the manual review of all documents, and automated methods are necessary for identifying high quality articles as a major filtering step. This work aimed to improve the performance and usability of existing tools with machine learning methods. First, evaluation methods for journals, articles, and websites were studied to determine if their performance varied widely for different topics. Second, the feasibility of predicting article citation count was examined by training Support Vector Machine (SVM) models on content and bibliometric features. Third, SVM models were used to automatically classify instrumental and non-instrumental citations.