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Data analytics in a clinical setting: Applications to understanding breathing patterns and their relevance to neonatal disease.

In this review, we focus on the use of contemporary linear and non-linear data analytics as well as machine learning/artificial intelligence algorithms to inform treatment of pediatric patients. We specifically focus on methods used to quantify changes in breathing that can lead to increased risk fo...

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Published in:Seminars in Fetal & Neonatal Medicine Vol. 27; no. 5
Main Authors: Wilson, Christopher G., Altamirano, A. Erika, Hillman, Tyler, Tan, John B.
Format: review Journal Article
Published: Elsevier B.V. Oct2022
Online Access:View this record in EBSCOhost
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      dt: Oct2022
      vid: 27
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      pub: Elsevier B.V.
      place: New York, New York
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        10.1016/j.siny.2022.101399
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        atl: Data analytics in a clinical setting: Applications to understanding breathing patterns and their relevance to neonatal disease.
      aug:
        au:
          Wilson, Christopher G.
          Altamirano, A. Erika
          Hillman, Tyler
          Tan, John B.
        affil: Lawrence D. Longo, MD Center for Perinatal Biology, Loma Linda University, School of Medicine, Loma Linda, CA, 92350, USA
      sug:
        subj:
          Infant, Newborn, Diseases
          Enterocolitis, Necrotizing
          Female
          Respiration
          Child
          Infant, Newborn
          Pregnancy
          Artificial Intelligence
          Scales
          Child: 6-12 years
          Infant, Newborn: birth-1 month
          Female
      ab: In this review, we focus on the use of contemporary linear and non-linear data analytics as well as machine learning/artificial intelligence algorithms to inform treatment of pediatric patients. We specifically focus on methods used to quantify changes in breathing that can lead to increased risk for apnea of prematurity, retinopathy of prematurity (ROP), necrotizing enterocolitis (NEC) and provide a list of potentially useful algorithms that comprise a suite of software tools to enhance prediction of outcome. Next, we provide a brief overview of machine learning/artificial intelligence methods and applications within the sphere of perinatal care. Finally, we provide an overview of the infrastructure needed to use these tools in a clinical setting for real-time data acquisition, data synchrony, data storage and access, and bedside data visualization to assist in clinical decision making and support the medical informatics mission. Our goal is to provide an overview and inspire other investigators to adopt these tools for their own research and optimization of perinatal patient care.
      pubtype: Academic Journal
      doctype:
        review
        Journal Article
      ougenre: Article
    language: English
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