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Data analytics in a clinical setting: Applications to understanding breathing patterns and their relevance to
Christopher G Wilson1, A Erika Altamirano2, Tyler Hillman2
1Lawrence D. Longo, MD Center for Perinatal Biology, Loma Linda University, School of Medicine, Loma Linda, CA, 92350, USA; Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA, 92350, USA.
Insights
This review explores using advanced data analytics and AI to improve pediatric patient care, focusing on predicting risks like apnea and retinopathy of prematurity (ROP) and necrotizing enterocolitis (NEC). It highlights infrastructure needs for clinical implementation.
Area of Science:
- Neonatal Medicine
- Medical Informatics
- Data Science
Background:
- Pediatric patient care faces challenges in predicting and managing risks associated with prematurity.
- Current methods for monitoring and predicting conditions like apnea of prematurity, retinopathy of prematurity (ROP), and necrotizing enterocolitis (NEC) can be improved.
Purpose of the Study:
- To review the application of data analytics and machine learning/artificial intelligence (AI) in perinatal care.
- To identify algorithms for quantifying breathing changes and predicting adverse outcomes in pediatric patients.
- To outline the necessary infrastructure for integrating these AI tools into clinical settings.
Main Methods:
- Review of contemporary linear and non-linear data analytics techniques.
- Exploration of machine learning/AI algorithms for risk prediction in neonates.
- Discussion of infrastructure requirements for clinical data management and visualization.
Main Results:
- Identification of specific algorithms applicable to quantifying breathing patterns and predicting ROP, NEC, and apnea of prematurity.
- Overview of AI/ML methods relevant to perinatal care.
- Description of essential infrastructure for real-time data acquisition, synchronization, storage, and bedside visualization.
Conclusions:
- Advanced data analytics and AI offer significant potential to enhance the prediction of outcomes and optimize care for pediatric patients, particularly neonates.
- Successful clinical implementation requires robust infrastructure for data handling and real-time decision support.
- Further adoption of these tools by researchers is encouraged to advance perinatal patient care.
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.
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