Machine Learning Versus Standard Techniques for Updating Searches for Systematic Reviews: A Diagnostic Accuracy Study
Authors: Paul G. Shekelle, Kanaka Shetty, Sydne Newberry, Margaret Maglione, Aneesa Motala
Journal: Annals of Internal Medicine (2017)

Letters1 August 2017Machine Learning Versus Standard Techniques for Updating Searches for Systematic Reviews: A Diagnostic Accuracy StudyPaul G. Shekelle, MD, PhD, Kanaka Shetty, MD, Sydne Newberry, PhD, Margaret Maglione, MPP, and Aneesa Motala, BAPaul G. Shekelle, MD, PhDFrom RAND Corporation, Santa Monica, California., Kanaka Shetty, MDFrom RAND Corporation, Santa Monica, California., Sydne Newberry, PhDFrom RAND Corporation, Santa Monica, California., Margaret Maglione, MPPFrom RAND Corporation, Santa Monica, California., and Aneesa Motala, BAFrom RAND Corporation, Santa Monica, California.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/L17-0124 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Background: Systematic reviews are a cornerstone of evidence-based care and a necessary foundation for care recommendations to be labeled clinical practice guidelines. However, they become outdated relatively quickly and require substantial resources to maintain relevance. One particularly time-consuming task is updating the search to identify relevant articles published since the last search.We previously tested machine-learning approaches for making screening for updating more efficient by using 2 clinical topics as examples: medications to treat low bone density and off-label use of atypical antipsychotics. We tested 2 machine-learning algorithms: a generalized linear model with convex penalties (glmnet, R Foundation for Statistical ...References1. Dalal SR, Shekelle PG, Hempel S, Newberry SJ, Motala A, Shetty KD. A pilot study using machine learning and domain knowledge to facilitate comparative effectiveness review updating. Med Decis Making. 2013;33:343-55. [PMID: 22961102] doi:10.1177/0272989X12457243 CrossrefMedlineGoogle Scholar2. Joachims T. Text categorization with support vector machines: learning with many relevant features [Abstract]. 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[PMID: 25588314] doi:10.1186/2046-4053-4-5 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From RAND Corporation, Santa Monica, California.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L17-0124.This article was published at Annals.org on 13 June 2017. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoComputer-Aided Systematic Review Screening Comes of Age Brian J. 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Hemens, BScPhm, MSc, RPh and Alfonso Iorio, MD, PhD 1 August 2017Volume 167, Issue 3Page: 213-215KeywordsAlgorithmsBone densityDisclosureGoutMachine learningPositive predictive valueSkin reactionSpecificitySystematic reviewsTreatment guidelines ePublished: 13 June 2017 Issue Published: 1 August 2017 Copyright & PermissionsCopyright © 2017 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...