Prediction of Adult Height by Machine Learning Technique.

Journal: The Journal of clinical endocrinology and metabolism
PMID: 33606028

Abstract

CONTEXT: Prediction of AH is frequently undertaken in the clinical setting. The commonly used methods are based on the assessment of skeletal maturation. Predictive algorithms generated by machine learning, which can already automatically drive cars and recognize spoken language, are the keys to unlocking data that can precisely inform the pediatrician for real-time decision making.

Authors

  • Michael Shmoish
    Bioinformatics Knowledge Unit, The Lokey Center, Technion-Israel Institute of Technology, Haifa, Israel.
  • Alina German
    Pediatric Endocrinology, Clalit Health Service, Haifa, Israel.
  • Nurit Devir
    Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel.
  • Anna Hecht
    Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel.
  • Gary Butler
    University College London Great Ormond Street Institute of Child Health, London, UK.
  • Aimon Niklasson
    Göteborg Pediatric Growth Research Center, Department of Pediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Kerstin Albertsson-Wikland
    Physiology/Endocrinology, Institute of Neuroscience & Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Ze'ev Hochberg
    The Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.

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