Machine learning for predicting severe dengue in Puerto Rico.

Journal: Infectious diseases of poverty
PMID: 39905498

Abstract

BACKGROUND: Distinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. World Health Organization (WHO)-recommended warning signs offer a practical approach for clinicians but have limited sensitivity and specificity. This study aims to evaluate machine learning (ML) model performance compared to WHO-recommended warning signs in predicting severe dengue among laboratory-confirmed cases in Puerto Rico.

Authors

  • Zachary J Madewell
    Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA. ock0@cdc.gov.
  • Dania M Rodriguez
    Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA.
  • Maile B Thayer
    Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA.
  • Vanessa Rivera-Amill
    Ponce Health Sciences University/Ponce Research Institute, Ponce, Puerto Rico, USA.
  • Gabriela Paz-Bailey
    Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA.
  • Laura E Adams
    Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA.
  • Joshua M Wong
    Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA.

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