Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study.

Journal: BMJ open
Published Date: Apr 1, 2022

Abstract

OBJECTIVES: To develop and validate tests to assess the risk of any cancer for patients referred to the NHS Urgent Suspected Cancer (2-week wait, 2WW) clinical pathways.

Authors

  • Richard Savage
    PinPoint Data Science Ltd, Leeds, UK rich.savage@pinpointdatascience.com.
  • Mike Messenger
    University of Leeds, Leeds, UK.
  • Richard D Neal
    University of Leeds, Leeds, UK.
  • Rosie Ferguson
    PinPoint Data Science Ltd, Leeds, UK.
  • Colin Johnston
    Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • Katherine L Lloyd
    PinPoint Data Science Ltd, Leeds, UK.
  • Matthew D Neal
    PinPoint Data Science Ltd, Leeds, UK.
  • Nigel Sansom
    PinPoint Data Science Ltd, Leeds, UK.
  • Peter Selby
    Department of Medicine, Manchester Royal Infirmary, Manchester, UK.
  • Nisha Sharma
    Leeds Teaching Hospital NHS Trust, Department of Radiology, Leeds, UK.
  • Bethany Shinkins
    University of Leeds, Leeds, UK.
  • Jim R Skinner
    PinPoint Data Science Ltd, Leeds, UK.
  • Giles Tully
    PinPoint Data Science Ltd, Leeds, UK.
  • Sean Duffy
    Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • Geoff Hall
    Leeds Teaching Hospitals Trust, Leeds, UK.

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