Artificial intelligence and machine learning in pediatrics and neonatology healthcare

Matsushita, Felipe Yu; Krebs, Vera Lúcia Jornada; Carvalho, Werther Brunow de

doi:10.1590/1806-9282.20220177

Cited by 8 publications

(4 citation statements)

References 26 publications

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“… 11 Due to growing computer power and the massive healthcare data generation, machine learning is now increasingly frequently employed to predict outcomes in medicine. 12 …”

Section: Discussionmentioning

confidence: 99%

Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms

Matsushita¹,

Krebs²,

Carvalho³

2023

Clinics

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“… 11 Due to growing computer power and the massive healthcare data generation, machine learning is now increasingly frequently employed to predict outcomes in medicine. 12 …”

Section: Discussionmentioning

confidence: 99%

Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms

Matsushita¹,

Krebs²,

Carvalho³

2023

Clinics

Self Cite

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“…Cutting-edge technologies are leading to a profound transformation of healthcare (1,2). Among the most promising advancements is the integration of artificial intelligence (AI) into the delicate and critical domain of pediatrics (3)(4)(5). We find ourselves at the threshold of a new frontier, envisioning how AI might revolutionize every step of the pediatric patient journey.…”

Section: Introductionmentioning

confidence: 99%

Editorial: Artificial intelligence and machine learning in pediatric surgery

Verhoeven,

Hulscher

2024

Front. Pediatr.

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“…This is achieved by establishing associations between essential data points, eliminating the need for prior interpretations or assumptions required for traditional data handling models. [ 6 ] Innovative AI-based digital technologies can provide at-home wearable solutions to obtain, track, and analyze data, as well as offer management advice based on algorithms and neural networks. [ 7 ]…”

Section: Introductionmentioning

confidence: 99%

Applications of artificial intelligence in the management of childhood obesity

Alghalyini

2023

Journal of Family Medicine and Primary Care

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Background: Childhood obesity has emerged as a significant public health challenge, with long-term implications that often extend into adulthood, increasing the susceptibility to chronic health conditions. Objective: The objective of this review is to elucidate the applications of artificial intelligence (AI) in the prevention and treatment of pediatric obesity, emphasizing its potential to complement and enhance traditional management methods. Methods: We undertook a comprehensive examination of existing literature to understand the integration of machine learning and other AI techniques in childhood obesity management strategies. Results: The findings from numerous studies suggest a strong endorsement for AI's role in addressing childhood obesity. Particularly, machine learning techniques have shown considerable efficacy in augmenting current therapeutic and preventive approaches. Conclusion: The intersection of AI with conventional obesity management practices presents a novel and promising approach to fortify interventions targeting pediatric obesity. This review accentuates the transformative capacity of AI, thereby advocating for continued research and innovation in this rapidly evolving domain.

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Artificial intelligence and machine learning in pediatrics and neonatology healthcare

Cited by 8 publications

References 26 publications

Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms

Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms

Editorial: Artificial intelligence and machine learning in pediatric surgery

Applications of artificial intelligence in the management of childhood obesity

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