title: Optimizing feature representation for automated systematic review work prioritization.
abstract: Automated document classification can be a valuable tool for enhancing the efficiency of creating and updating systematic reviews (SRs) for evidence-based medicine. One way document classification can help is in performing work prioritization: given a set of documents, order them such that the most likely useful documents appear first. We evaluated several alternate classification feature systems including unigram, n-gram, MeSH, and natural language processing (NLP) feature sets for their usefulness on 15 SR tasks, using the area under the receiver operating curve as a measure of goodness. We also examined the impact of topic-specific training data compared to general SR inclusion data. The best feature set used a combination of n-gram and MeSH features. NLP-based features were not found to improve performance. Furthermore, topic-specific training data usually provides a significant performance gain over more general SR training.
authors.affiliation: Department of Medical Informatics and Clinical Epidemiology,Oregon Health & Science University, Portland, Oregon, USA.
authors.forename: Aaron M
authors.initials: AM
authors.lastname: Cohen
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods; D001185:Artificial Intelligence; D004282:Documentation / Q000145:classification / Q000379:methods; D019317:Evidence-Based Medicine; D006292:Health Priorities; D009323:Natural Language Processing; D009922:Oregon; D010363:Pattern Recognition, Automated / Q000379:methods; D016526:Workload; D000078202:Systematic Reviews as Topic