Machine learning to predict stroke risk from routine hospital data: A systematic review.

Journal: International journal of medical informatics
PMID: 39908727

Abstract

PURPOSE: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHADS-VASc and QRISK3 are of limited accuracy, particularly in those without a diagnosis of atrial-fibrillation. Hence, there is a need for more accurate stroke risk prediction models. Machine-learning (ML) may provide a solution to this by leveraging existing routine hospital databases to build accurate stroke risk prediction models and identify novel risk factors for stroke.

Authors

  • William Heseltine-Carp
    University of Plymouth, Room N6, ITTC Building, Plymouth Science Park, Plymouth PL68BX, UK. Electronic address: w.heseltine-carp@nhs.net.
  • Megan Courtman
    University of Plymouth, Room N6, ITTC Building, Plymouth Science Park, Plymouth PL68BX, UK; University of Plymouth, Plymouth PL4 8AA, UK. Electronic address: megan.courtman@plymouth.ac.uk.
  • Daniel Browning
    University of Plymouth, Room N6, ITTC Building, Plymouth Science Park, Plymouth PL68BX, UK. Electronic address: daniel.browning@nhs.net.
  • Aishwarya Kasabe
    University of Plymouth, Room N6, ITTC Building, Plymouth Science Park, Plymouth PL68BX, UK. Electronic address: aishwarya.kasabe@plymouth.ac.uk.
  • Michael Allen
    University of Exeter Medical School, Exeter, UK and NIHR South West Peninsula Applied Research Collaboration (ARC), Exeter, UK.
  • Adam Streeter
    University of Plymouth, N15, ITTC1, Plymouth Science Park, Plymouth PL6 8BX, UK. Electronic address: adam.streeter@plymouth.ac.uk.
  • Emmanuel Ifeachor
    University of Plymouth, N15, ITTC1, Plymouth Science Park, Plymouth PL6 8BX, UK; School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK. Electronic address: e.ifeachor@plymouth.ac.uk.
  • Martin James
    University of Exeter Medical School, Exeter, UK and NIHR South West Peninsula Applied Research Collaboration (ARC), Exeter, UK.
  • Stephen Mullin
    University of Plymouth, Room N6, ITTC Building, Plymouth Science Park, Plymouth PL68BX, UK. Electronic address: stephen.mullin@plymouth.ac.uk.

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