Data analytics in a clinical setting: Applications to understanding breathing patterns and their relevance to neonatal disease. Issue 5 (October 2022)

• Record Type: Journal Article
• Authors: Wilson, Christopher G.; Altamirano, A. Erika; Hillman, Tyler; Tan, John B.
• Is Part Of: Seminars in fetal & neonatal medicine. Volume 27:Issue 5(2022)
• Publication Date: 2022-10
• Subjects: Machine learning -- Medical informatics -- Predictive models -- Healthcare engineering; Neonatology -- Periodicals; Newborn infants -- Diseases -- Periodicals; Fetus -- Diseases -- Periodicals; 618.9201
• DOI: 10.1016/j.siny.2022.101399
• Languages: English
• ISSNs: 1744-165X

Abstract

Abstract: 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. Highlights: NICU data infrastructures need to be improved. The use of the latest bedside technology can greatly benefit and inform current clinical care. Adoption of contemporary medical informatics tools, including machine learning, will allow practitioners to improve patient outcomes. Higher resolution, real-time data acquisition is not yet widely available in the NICU but will need to be available for next-generation medical informatics. Using non-linear, dynamical systems and information theory metrics applied to longitudinal data is an unexplored opportunity. The use of machine learning in elucidating multi-systemic neonatal diseases will be the next step for improving outcome for very sick babies in the NICU.