Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

• Andrew J. Steele ,

  Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

  * E-mail: statto+plosone@andrewsteele.co.uk

  Affiliation The Francis Crick Institute, London, United Kingdom

  http://orcid.org/0000-0002-6836-5925

  ⨯
• Spiros C. Denaxas,

  Roles Conceptualization, Data curation, Investigation, Project administration, Supervision, Writing – review & editing

  Affiliation Farr Institute of Health Informatics Research, Institute of Health Informatics, University College London, London, United Kingdom

  ⨯
• Anoop D. Shah,

  Roles Conceptualization, Data curation, Investigation, Writing – review & editing

  Affiliations Farr Institute of Health Informatics Research, Institute of Health Informatics, University College London, London, United Kingdom, University College London Hospitals NHS Foundation Trust, London, United Kingdom

  ⨯
• Harry Hemingway,

  Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

  Affiliation Farr Institute of Health Informatics Research, Institute of Health Informatics, University College London, London, United Kingdom

  ⨯
• Nicholas M. Luscombe

  Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

  Affiliations The Francis Crick Institute, London, United Kingdom, UCL Genetics Institute, Department of Genetics Evolution and Environment, University College London, London, United Kingdom, Okinawa Institute of Science & Technology Graduate University, Okinawa, Japan

  ⨯