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An automated approach for ranking journals to help in clinician decision support
Siddhartha R Jonnalagadda1, Soheil Moosavinasab2, Chinmoy Nath3
1Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL ; Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
Abstract:
Point of care access to knowledge from full text journal articles supports decision-making and decreases medical errors. However, it is an overwhelming task to search through full text journal articles and find quality information needed by clinicians. We developed a method to rate journals for a given clinical topic, Congestive Heart Failure (CHF). Our method enables filtering of journals and ranking of journal articles based on source journal in relation to CHF. We also obtained a journal priority score, which automatically rates any journal based on its importance to CHF. Comparing our ranking with data gathered by surveying 169 cardiologists, who publish on CHF, our best Multiple Linear Regression model showed a correlation of 0.880, based on five-fold cross validation. Our ranking system can be extended to other clinical topics.
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