Machine learning for hospital readmission prediction in pediatric population

Silva, Nayara Cristina da; Albertini, Marcelo Keese; Backes, André Ricardo; Pena, Geórgia das Graças

doi:10.1016/j.cmpb.2023.107980

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…It refers to the field of study that focuses on algorithms and models capable of automatically learning from data and making predictions or decisions without being explicitly programmed. ML techniques in healthcare leverage the power of ANN and DLN models, along with other approaches such as decision tree [22], support vector machine [23], and random forest [24]. These techniques enable the extraction of valuable insights from healthcare data, facilitating tasks such as patient risk stratification, treatment recommendation, and health outcome prediction.…”

Section: Key Principles Of Ai Within the Context Of Healthcarementioning

confidence: 99%

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Bekbolatova,

Mayer,

Ong

et al. 2024

Healthcare

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Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

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Section: Key Principles Of Ai Within the Context Of Healthcarementioning

confidence: 99%

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Bekbolatova,

Mayer,

Ong

et al. 2024

Healthcare

View full text Add to dashboard Cite

show abstract

“…Tackling these intricacies requires a holistic approach that leverages cutting-edge solutions and embraces technological advancements to drive positive change and foster a resilient future for our planet. In this context, machine learning (ML) emerges as a potent force, enabling data-driven insights and solutions across diverse domains such as renewable energy optimization [4], climate change mitigation [5], and personalized healthcare [6]. However, traditional centralized ML approaches encounter limitations, particularly concerning data privacy concerns and resource constraints, especially in geographically distributed settings [7].…”

Section: Introductionmentioning

confidence: 99%

A Communication-Efficient Federated Learning Framework for Sustainable Development Using Lemurs Optimizer

Al-Betar,

Abasi,

Alyasseri

et al. 2024

Algorithms

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The pressing need for sustainable development solutions necessitates innovative data-driven tools. Machine learning (ML) offers significant potential, but faces challenges in centralized approaches, particularly concerning data privacy and resource constraints in geographically dispersed settings. Federated learning (FL) emerges as a transformative paradigm for sustainable development by decentralizing ML training to edge devices. However, communication bottlenecks hinder its scalability and sustainability. This paper introduces an innovative FL framework that enhances communication efficiency. The proposed framework addresses the communication bottleneck by harnessing the power of the Lemurs optimizer (LO), a nature-inspired metaheuristic algorithm. Inspired by the cooperative foraging behavior of lemurs, the LO strategically selects the most relevant model updates for communication, significantly reducing communication overhead. The framework was rigorously evaluated on CIFAR-10, MNIST, rice leaf disease, and waste recycling plant datasets representing various areas of sustainable development. Experimental results demonstrate that the proposed framework reduces communication overhead by over 15% on average compared to baseline FL approaches, while maintaining high model accuracy. This breakthrough extends the applicability of FL to resource-constrained environments, paving the way for more scalable and sustainable solutions for real-world initiatives.

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Applications of Real‐Time Data in Healthcare Interventions

Thorat,

Kakade

2024

Internet of Medicine for Smart Healthcare

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Machine learning for hospital readmission prediction in pediatric population

Cited by 7 publications

References 40 publications

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

A Communication-Efficient Federated Learning Framework for Sustainable Development Using Lemurs Optimizer

Applications of Real‐Time Data in Healthcare Interventions

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