An interpretable predictive deep learning platform for pediatric metabolic diseases.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 38497983

Abstract

OBJECTIVES: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications.

Authors

  • Hamed Javidi
    Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, United States.
  • Arshiya Mariam
    Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, United States.
  • Lina Alkhaled
    Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, OH 44195, United States.
  • Kevin M Pantalone
    Department of Endocrinology, Endocrinology & Metabolism Institute, Cleveland Clinic, Cleveland, OH.
  • Daniel M Rotroff
    Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, United States.

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