title: Prospective validation of text categorization filters for identifying high-quality, content-specific articles in MEDLINE.
abstract: In prior work, we introduced a machine learning method to identify high quality MEDLINE documents in internal medicine. The performance of the original filter models built with this corpus on years outside 1998-2000 was not assessed directly. Validating the performance of the original filter models on current corpora is crucial to validate them for use in current years, to verify that the model fitting and model error estimation procedures do not over-fit the models, and to validate consistency of the chosen ACPJ gold standard (i.e., that ACPJ editorial policies and criteria are stable over time). Our prospective validation results indicated that in the categories of treatment, etiology, diagnosis, and prognosis, the original machine learning filter models built from the 1998-2000 corpora maintained their discriminatory performance of 0.97, 0.97, 0.94, and 0.94 area under the curve in each respective category when applied to a 2005 corpus. The ACPJ is a stable, reliable gold standard and the machine learning methodology provides robust models and model performance estimates. Machine learning filter models built with 1998-2000 corpora can be applied to identify high quality articles in recent years.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
authors.forename: Yindalon
authors.initials: Y
authors.lastname: Aphinyanaphongs
authors.forename: Constantin
authors.initials: C
authors.lastname: Aliferis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D046650:Medical Subject Headings; D039781:PubMed; D012372:ROC Curve