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Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers.

Steven HawkenRobin DucharmeMalia S Q MurphyBrieanne OlibrisA Brianne BotaLindsay A WilsonWei ChengJulian LittleBeth K PotterKathryn M DenizeMonica LamoureuxMatthew HendersonKatelyn J RittenhouseJoan T PriceHumphrey MwapeBellington VwalikaPatrick MusondaJesmin PervinA K Azad ChowdhuryAnisur RahmanPranesh ChakrabortyJeffrey S A StringerKumanan Wilson
Published in: PloS one (2023)
Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.
Keyphrases
  • machine learning
  • big data
  • cord blood
  • gestational age
  • electronic health record
  • deep learning
  • artificial intelligence
  • pet ct
  • birth weight
  • preterm infants
  • high resolution
  • mass spectrometry
  • body mass index
  • weight loss