A comparison of citation metrics to machine learning filters for the identification of high quality MEDLINE documents.
The present study explores the discriminatory performance of existing and novel gold-standard-specific machine learning (GSS-ML) focused filter models (i.e., models built specifically for a retrieval task and a gold standard against which they are evaluated) and compares their performance to citation count and impact factors, and non-specific machine learning (NS-ML) models (i.e., models built for a different task and/or different gold standard).
Author(s): Aphinyanaphongs, Yindalon, Statnikov, Alexander, Aliferis, Constantin F
DOI: 10.1197/jamia.M2031