Machine Learning and Evidence-Based Medicine

Abstract

Ideas and Opinions3 July 2018Machine Learning and Evidence-Based MedicineIan A. Scott, MBBS, MHA, MEdIan A. Scott, MBBS, MHA, MEdUniversity of Queensland, Brisbane, Queensland, Australia (I.A.S.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M18-0115 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Machine learning (ML), which converts complex data into algorithms, challenges the traditional epidemiologic approach of evidence-based medicine (EBM). Here I outline the differences, strengths, and limitations of these 2 approaches and suggest areas of reconciliation.A Historical ContextBeginning in the 1970s, scientists extolled the virtues of EBM's hypothesis-driven, protocolized experiments involving well-defined populations and preselected exposure and outcome variables. Inferences were made using traditional biostatistics. In the early 1990s, ML emerged, whereby advanced computing programs (machines) processed huge data sets (big data) from many sources and discerned patterns among multiple unselected variables. Such patterns were undiscoverable using traditional biostatistics ...References1. Alanazi HO, Abdullah AH, Qureshi KN. A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J Med Syst. 2017;41:69. [PMID: 28285459] CrossrefMedlineGoogle Scholar2. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018. [PMID: 29389679] CrossrefMedlineGoogle Scholar3. Bruland P, McGilchrist M, Zapletal E, Acosta D, Proeve J, Askin S, et al. Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. BMC Med Res Methodol. 2016;16:159. [PMID: 27875988] CrossrefMedlineGoogle Scholar4. 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[PMID: 29166301] doi:10.1097/EDE.0000000000000787 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: University of Queensland, Brisbane, Queensland, Australia (I.A.S.)Acknowledgment: The author thanks Prof. Paul Glasziou, Director, Centre for Research in Evidence-Based Practice, Bond University, Gold Coast, Australia, and Prof. Adam Elshaug, Co-Director, Menzies Centre for Health Policy, University of Sydney, Sydney, Australia, for helpful comments on previous drafts of the manuscript.Disclosures: The author has disclosed no conflicts of interest. The form can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-0115.Corresponding Author: Ian A. Scott, MBBS, MHA, MEd, University of Queensland, Translational Research Institute, 37 Kent Street, Brisbane, Queensland 4102, Australia; e-mail, ian.[email protected]qld.gov.au.Author Contributions: Conception and design: I.A. Scott.Analysis and interpretation of the data: I.A. Scott.Drafting of the article: I.A. Scott.Critical revision of the article for important intellectual content: I.A. Scott.Final approval of the article: I.A. Scott.Statistical expertise: I.A. Scott.Administrative, technical, or logistic support: I.A. Scott.Collection and assembly of data: I.A. Scott.This article was published at Annals.org on 1 May 2018. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byWritten by Humans or Artificial Intelligence? 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