Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches

• Stephen F. Weng ,

  Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

  * E-mail: stephen.weng@nottingham.ac.uk

  Affiliations NIHR School for Primary Care Research, University of Nottingham, Nottingham, United Kingdom, Primary Care Stratified Medicine (PRISM), Division of Primary Care, School of Medicine, University of Nottingham, Nottingham United Kingdom

  http://orcid.org/0000-0002-5281-9590

  ⨯
• Luis Vaz,

  Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Writing – review & editing

  Affiliation NIHR School for Primary Care Research, University of Nottingham, Nottingham, United Kingdom

  http://orcid.org/0000-0002-2184-1234

  ⨯
• Nadeem Qureshi ,

  Contributed equally to this work with: Nadeem Qureshi, Joe Kai

  Roles Investigation, Supervision, Validation, Visualization, Writing – review & editing

  Affiliations NIHR School for Primary Care Research, University of Nottingham, Nottingham, United Kingdom, Primary Care Stratified Medicine (PRISM), Division of Primary Care, School of Medicine, University of Nottingham, Nottingham United Kingdom

  http://orcid.org/0000-0003-4909-0644

  ⨯
• Joe Kai

  Contributed equally to this work with: Nadeem Qureshi, Joe Kai

  Roles Investigation, Methodology, Supervision, Validation, Visualization, Writing – review & editing

  Affiliations NIHR School for Primary Care Research, University of Nottingham, Nottingham, United Kingdom, Primary Care Stratified Medicine (PRISM), Division of Primary Care, School of Medicine, University of Nottingham, Nottingham United Kingdom

  ⨯