Machine Learning to Improve Accuracy of Transcutaneous Bilirubinometry.

Journal: Neonatology
PMID: 38684146

Abstract

INTRODUCTION: This study aimed to develop models for predicting total serum bilirubin by correcting errors of transcutaneous bilirubin using machine learning based on neonatal biomarkers that could affect spectrophotometric measurements of tissue bilirubin.

Authors

  • Daisaku Morimoto
    Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan, daisaku_morimoto@yahoo.co.jp.
  • Yosuke Washio
    Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
  • Kana Fukuda
    Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
  • Takeshi Sato
    Department of Obstetrics and Gynecology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan. Electronic address: og.sato@med.nagoya-cu.ac.jp.
  • Tomoka Okamura
    Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
  • Hirokazu Watanabe
    Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
  • Junko Yoshimoto
    Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
  • Maki Tanioka
    Clinical AI Human Resources Development Program, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
  • Hirokazu Tsukahara
    Department of Pediatrics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.

Keywords

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