Application of Statistical Analysis and Machine Learning to Identify Infants' Abnormal Suckling Behavior.

Journal: IEEE journal of translational engineering in health and medicine
PMID: 38765888

Abstract

OBJECTIVE: Identify infants with abnormal suckling behavior from simple non-nutritive suckling devices.

Authors

  • Phuong Truong
    Medically Advanced Devices LaboratoryDepartment of Mechanical and Aerospace EngineeringJacobs School of Engineering, University of California at San Diego La Jolla CA 92093 USA.
  • Erin Walsh
    Center for Voice and SwallowingDepartment of OtolaryngologySchool of Medicine, University of California at San Diego La Jolla CA 92122 USA.
  • Vanessa P Scott
    Department of PediatricsSchool of MedicineUniversity of California at San Diego La Jolla CA 92037 USA.
  • Michelle Leff
    Department of PediatricsSchool of MedicineUniversity of California at San Diego La Jolla CA 92037 USA.
  • Alice Chen
    AP Chen Consultant, Potomac, MD, United States.
  • James Friend
    Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, USA. jfriend@ucsd.edu.

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