Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments.

Journal: BMC cancer
PMID: 33187484

Abstract

BACKGROUND: Objectives were to build a machine learning algorithm to identify bloodstream infection (BSI) among pediatric patients with cancer and hematopoietic stem cell transplantation (HSCT) recipients, and to compare this approach with presence of neutropenia to identify BSI.

Authors

  • Lillian Sung
    Division of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G1X8, Canada. lillian.sung@sickkids.ca.
  • Conor Corbin
    Department of Biomedical Data Science, Stanford University, Palo Alto, CA, United States.
  • Ethan Steinberg
    Biomedical Informatics Research, Stanford University, Palo Alto, USA.
  • Emily Vettese
    Division of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G1X8, Canada.
  • Aaron Campigotto
    Division of Infectious Diseases, The Hospital for Sick Children, Toronto, Canada.
  • Loreto Lecce
    Division of Neonatology, The Hospital for Sick Children, Toronto, Canada.
  • George A Tomlinson
    Department of Medicine, University Health Network, Toronto, Canada.
  • Nigam Shah
    Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, United States.

Keywords

External Resources

Popular Topics
Recent Journals