Warning: Undefined variable $zfal in C:\Inetpub\vhosts\kidney.de\httpdocs\mlpefetch.php on line 525
Deprecated: str_replace(): Passing null to parameter #3 ($subject) of type array|string is deprecated in C:\Inetpub\vhosts\kidney.de\httpdocs\mlpefetch.php on line 525
Warning: Undefined variable $sterm in C:\Inetpub\vhosts\kidney.de\httpdocs\mlpefetch.php on line 530
free
Warning: Undefined variable $sterm in C:\Inetpub\vhosts\kidney.de\httpdocs\mlpefetch.php on line 531
free free
English Wikipedia
Nephropedia Template TP (
Twit Text
DeepDyve Pubget Overpricing |
lüll Text categorization models for retrieval of high quality articles in internal medicine Aphinyanaphongs Y; Aliferis CFAMIA Annu Symp Proc 2003[]; 2003 (ä): 31-5The discipline of Evidence Based Medicine (EBM) studies formal and quasi-formal methods for identifying high quality medical information and abstracting it in useful forms so that patients receive the best customized care possible [1]. Current computer-based methods for finding high quality information in PubMed and similar bibliographic resources utilize search tools that employ preconstructed Boolean queries. These clinical queries are derived from a combined application of (a) user interviews, (b) ad-hoc manual document quality review, and (c) search over a constrained space of disjunctive Boolean queries. The present research explores the use of powerful text categorization (machine learning) methods to identify content-specific and high-quality PubMed articles. Our results show that models built with the proposed approach outperform the Boolean based PubMed clinical query filters in discriminatory power.|*Internal Medicine[MESH]|*PubMed[MESH]|Area Under Curve[MESH]|Artificial Intelligence[MESH]|Bayes Theorem[MESH]|Evidence-Based Medicine[MESH]|Information Storage and Retrieval/*methods[MESH]|Models, Theoretical[MESH] |