Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

Journal: Journal of medical Internet research
PMID: 32401216

Abstract

BACKGROUND: Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may progress to organ failure during disease exacerbations. Early detection of acute physiological deterioration leading to organ failure is not always attainable. Machine learning techniques that allow for prediction of organ failure may enable early identification and treatment and potentially reduce mortality.

Authors

  • Akram Mohammed
    Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America.
  • Pradeep S B Podila
    Faith and Health Division, Methodist Le Bonheur Healthcare, Memphis, TN, United States.
  • Robert L Davis
    Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
  • Kenneth I Ataga
    Department of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA.
  • Jane S Hankins
    Department of Hematology, St Jude Children's Research Hospital, Memphis, TN, United States.
  • Rishikesan Kamaleswaran
    Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.

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