Supervised learning for infection risk inference using pathology data.

Journal: BMC medical informatics and decision making
Published Date: Dec 8, 2017

Abstract

BACKGROUND: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions.

Authors

  • Bernard Hernandez
    Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, B422 Bessemer Building, South Kensington Campus, London, SW7 2AZ, UK. b.hernandez-perez@imperial.ac.uk.
  • Pau Herrero
  • Timothy Miles Rawson
    Health Protection Unit in Healthcare Associated infections and Antimicrobial Resistance, Imperial College London, 8th floor Commonwealth Building, Hammersmith Hospital Campus, Acton, London, W12 0NN, UK.
  • Luke S P Moore
    Health Protection Unit in Healthcare Associated infections and Antimicrobial Resistance, Imperial College London, 8th floor Commonwealth Building, Hammersmith Hospital Campus, Acton, London, W12 0NN, UK.
  • Benjamin Evans
    Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, B422 Bessemer Building, South Kensington Campus, London, SW7 2AZ, UK.
  • Christofer Toumazou
  • Alison H Holmes
    Health Protection Unit in Healthcare Associated infections and Antimicrobial Resistance, Imperial College London, 8th floor Commonwealth Building, Hammersmith Hospital Campus, Acton, London, W12 0NN, UK.
  • Pantelis Georgiou

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