Using Machine Learning to Locate Evidence More Efficiently
OAI: oai:igi-global.com:302752 • DOI: 10.4018/978-1-7998-9702-6.ch008
Published by: IGI Global
Abstract
Evidence that machine learning can assist article selection and minimize manual screening burden for scholarly research has been documented in the peer-reviewed literature for more than 20 years. Despite the robust evidence and continual technological advances, uptake has been slow among research teams. This chapter discusses the benefits of using machine learning (ML) and other automation tools on bibliographic data and argues that academic librarians are well-positioned to partner with research teams around this application of ML. An overview of the automation approaches used at UNC's Health Sciences Library (HSL) is discussed along with detailed accounts of multiple success stories of when HSL librarians partnered with research teams to locate evidence more efficiently. Finally, a discussion of likely barriers and possible solutions to increase uptake of this technology among academic librarians is provided.