A new algorithm for reducing the workload of experts in performing systematic reviews.

ABSTRACT:

Objective

To determine whether a factorized version of the complement naïve Bayes (FCNB) classifier can reduce the time spent by experts reviewing journal articles for inclusion in systematic reviews of drug class efficacy for disease treatment.

Design

The proposed classifier was evaluated on a test collection built from 15 systematic drug class reviews used in previous work. The FCNB classifier was constructed to classify each article as containing high-quality, drug class-specific evidence or not. Weight engineering (WE) techniques were added to reduce underestimation for Medical Subject Headings (MeSH)-based and Publication Type (PubType)-based features. Cross-validation experiments were performed to evaluate the classifier's parameters and performance.

Measurements

SUBMITTER: Matwin S 

PROVIDER: S-EPMC2995653 | biostudies-literature | 2010 Jul-Aug

REPOSITORIES: biostudies-literature