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

Lillian SungConor CorbinEthan SteinbergEmily VetteseAaron CampigottoLoreto LecceGeorge A TomlinsonNigam Shah
Published in: BMC cancer (2020)
We developed a machine learning algorithm to classify BSI. GBM achieved an AUROC of 0.74 and identified 4.3% additional true cases in the test set. The machine learning algorithm did not perform substantially better than using presence of neutropenia alone to predict BSI.
Keyphrases
  • machine learning
  • artificial intelligence
  • big data
  • deep learning
  • papillary thyroid
  • squamous cell carcinoma
  • klebsiella pneumoniae
  • escherichia coli
  • childhood cancer
  • gram negative
  • multidrug resistant
  • neural network