Machine learning applications in critical care

Dhoayan, Mohammed Al; AlGhamdi, Huda; Arabi, Yaseen M.

doi:10.4103/2543-1854.259475

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The use of ML and AI for diagnosis purposes gains more importance and is more needed when they substitute traditional diagnosis tools that are either expensive, not accurate, require special skills, or take a long time to reveal results, which the latter is the case for Covid-19 [9]. Even more, ML algorithms that use unsupervised learning methods open opportunities for researchers and clinicians to investigate the undiscovered characteristics of this virus and what might be common for Covide-19 cases to generate hypotheses that can be further examined with confirmatory analysis.…”

Section: Applications For Clinical Diagnosis and Image Recognitionmentioning

confidence: 99%

The Role of Artificial Intelligence and Machine Learning During the Covid-19 Pandemic: A Review

Aldhoayan

2022

Studies in Health Technology and Informatics

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Covid-19 is one of the most significant infectious diseases that have faced humanity in the past century from clinical, economic, and social perspectives. Although the role of infectious diseases in human history has been vicious and is well known to humanity, Covid-19 is a special case since it is the first worldwide outbreak in the era of advanced computing and telecommunications. For this reason, it was only logical to see Artificial Intelligence (AI) and Machine Learning (ML) on the top of the list of controls to compact the spread of Covid-19. This paper goes through the applications of AI and ML that were reported in some of the major literature indexes and can be related to the main issues that face healthcare providers during the Covid-19 pandemic. This paper also discusses the applicability of these applications to healthcare organizations and points out the main prerequisites before they can be adopted.

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Section: Applications For Clinical Diagnosis and Image Recognitionmentioning

confidence: 99%

The Role of Artificial Intelligence and Machine Learning During the Covid-19 Pandemic: A Review

Aldhoayan

2022

Studies in Health Technology and Informatics

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Artificial Intelligence Applications in Neonatal Critical Care: A Scoping Review

Sakore,

Devi,

Mahapure

et al. 2024

Journal of Clinical Neonatology

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Background: The development of artificial intelligence (AI) approaches impacted drug discovery, medical imaging, customized diagnostics, and therapeutics. Medicine will be transformed by AI. One such area of medicine where AI is significantly improving care is neonatology. Objective: The objective of this scoping review is to explore the applications of AI in neonatal critical care and its outcome. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a scoping review was conducted utilizing the Web of Science, MEDLINE (PubMed), and Scopus databases. The search was limited to full-text publications on AI applications in neonatal critical care that were published between January 1, 2019, and December 31, 2023. Articles specifically addressing the application of AI in neonatal care have been considered within the scope of this review. At least three reviewers had independently executed the screening, data abstraction, and exploration. Results: Database searches yielded 631 articles, of which 11 met the inclusion criteria. The research encompassed extensive AI applications in neonatal critical care, employed for prognosis, diagnosis, and therapy strategizing. Artificial neural networks, machine learning, deep learning, and shallow hybrid neural networks were the commonly utilized AI techniques (neonatal critical care). These methods were applied to screen for inborn metabolic abnormalities, predict various outcomes, including death and sepsis, identify diseases such as sepsis, and assess neurodevelopmental outcomes in preterm newborns, helping plan several medical treatments. The included research demonstrated encouraging outcomes when using AI in neonatal critical care. Conclusions: AI-driven electronic arrangements upgrade neonatal basic care by improving risk forecast, promising critical commitments to future health care. Be that as it may, careful appraisal, evidence-based considers, and determination of safety, ethics, and information straightforwardness issues are essential before implementation. Acceptance by administrative bodies and the therapeutic community pivots on tending to these concerns.

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Machine learning applications in critical care

Cited by 2 publications

References 3 publications

The Role of Artificial Intelligence and Machine Learning During the Covid-19 Pandemic: A Review

The Role of Artificial Intelligence and Machine Learning During the Covid-19 Pandemic: A Review

Artificial Intelligence Applications in Neonatal Critical Care: A Scoping Review

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