Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.

Journal: PLoS neglected tropical diseases
PMID: 33362244

Abstract

BACKGROUND: Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue.

Authors

  • Sheng-Wen Huang
    National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Tainan, Taiwan.
  • Huey-Pin Tsai
    Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
  • Su-Jhen Hung
    National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Tainan, Taiwan.
  • Wen-Chien Ko
    Division of Infectious Diseases, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
  • Jen-Ren Wang
    Department of Pathology, National Cheng Kung University Hospital, and College of Medicine, National Cheng Kung University, Tainan, Taiwan.

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