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    Infants Sucking Pattern Identification Using Machine-Learned Computational Modeling

    Source: Journal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 008 ):;issue: 003::page 31003-1
    Author:
    Olapojoye, Abdullahi
    ,
    Singh, Abhishek
    ,
    Nishi, Eri
    ,
    Fei, Baowei
    ,
    Nostratinia, Aria
    ,
    Hassanipour, Fatemeh
    DOI: 10.1115/1.4066459
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Breastfeeding involves a complex coordination of swallowing, breathing, and sucking, with the infant's sucking proficiency being crucial for adequate nutrient intake. However, real-time assessment of milk intake is difficult, and issues with sucking often become apparent after the infant shows signs of nutrient deficiency. Traditional assessments by clinicians rely on the expertise and subjective judgment of healthcare professionals, enabling personalized evaluations. In this research, we introduce a novel approach to identifying sucking patterns by leveraging data collected from infants during breastfeeding sessions. This method utilizes artificial nipple-based sensors to capture the tongue forces exerted by infants, generating valuable clinical data. In the analysis of the collected time-series data, we applied machine-learned computational modeling (MLCM) algorithms to extract pertinent features and identify distinctive sucking patterns. The best-performing model demonstrated an accuracy of 90%, an 80% recall score, a perfect 100% precision score, a 0.90 f1-score, and an area under the curve (AUC) of 0.80. The proposed classification system has the potential to serve as a reliable decision-support tool for clinicians, offering valuable insights into infants' sucking behaviors. By integrating machine learning (ML)-based computational modeling into clinical practice, we aim to enhance the early identification of unhealthy sucking patterns, allowing for timely interventions and pro-active healthcare management.
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      Infants Sucking Pattern Identification Using Machine-Learned Computational Modeling

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4305776
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    • Journal of Engineering and Science in Medical Diagnostics and Therapy

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    contributor authorOlapojoye, Abdullahi
    contributor authorSingh, Abhishek
    contributor authorNishi, Eri
    contributor authorFei, Baowei
    contributor authorNostratinia, Aria
    contributor authorHassanipour, Fatemeh
    date accessioned2025-04-21T10:14:25Z
    date available2025-04-21T10:14:25Z
    date copyright9/30/2024 12:00:00 AM
    date issued2024
    identifier issn2572-7958
    identifier otherjesmdt_008_03_031003.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305776
    description abstractBreastfeeding involves a complex coordination of swallowing, breathing, and sucking, with the infant's sucking proficiency being crucial for adequate nutrient intake. However, real-time assessment of milk intake is difficult, and issues with sucking often become apparent after the infant shows signs of nutrient deficiency. Traditional assessments by clinicians rely on the expertise and subjective judgment of healthcare professionals, enabling personalized evaluations. In this research, we introduce a novel approach to identifying sucking patterns by leveraging data collected from infants during breastfeeding sessions. This method utilizes artificial nipple-based sensors to capture the tongue forces exerted by infants, generating valuable clinical data. In the analysis of the collected time-series data, we applied machine-learned computational modeling (MLCM) algorithms to extract pertinent features and identify distinctive sucking patterns. The best-performing model demonstrated an accuracy of 90%, an 80% recall score, a perfect 100% precision score, a 0.90 f1-score, and an area under the curve (AUC) of 0.80. The proposed classification system has the potential to serve as a reliable decision-support tool for clinicians, offering valuable insights into infants' sucking behaviors. By integrating machine learning (ML)-based computational modeling into clinical practice, we aim to enhance the early identification of unhealthy sucking patterns, allowing for timely interventions and pro-active healthcare management.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleInfants Sucking Pattern Identification Using Machine-Learned Computational Modeling
    typeJournal Paper
    journal volume8
    journal issue3
    journal titleJournal of Engineering and Science in Medical Diagnostics and Therapy
    identifier doi10.1115/1.4066459
    journal fristpage31003-1
    journal lastpage31003-8
    page8
    treeJournal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 008 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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