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A Machine Learning Approach for Rating the Quality of Depression Treatment Web Pages Yanjun Zhang1, Hong Cui2, Jacquelyn Burkell1 and Robert E. Mercer3 Faculty of Information & Media Studies, The University of Western Ontario School of Information Resources & Library Science, The University of Arizona 3 Department of Computer Science, The University of Western Ontario 1 2
Abstract As health care information proliferates on the web, the content quality is varied and difficult to assess, partially due to the large volume and the dynamicity. This paper reports an automated approach in which the quality of depression treatment web pages is assessed according to evidence-based depression treatment guidelines. A supervised machine learning technique, specifically Naive Bayes classification, is used to identify the sentences that are consistent with the guidelines. The quality score of a depression treatment web page is the number of unique evidence-based guidelines covered in this page. Significant Pearson correlation (p
