A Computational Proof of Concept of a Machine-Intelligent Artificial Pancreas Using Lyapunov Stability and Differential Game Theory

Greenwood, Nigel; Gunton, Jenny E.

doi:10.1177/1932296814536271

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…Optimization of AI techniques, aside from their use in conjunction with AI controllers, have been used in other studies to determine controller parameters. For example, Tang et al used GA to mine information from patients’ medical histories to generate multiple customized models [ 30 ], and Greenwood et al proposed the use of particle swarm optimization to adjust the function cost of an economic predictive control model [ 31 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

365

207

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BackgroundArtificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.ObjectiveThe objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.MethodsA review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.ResultsWe propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.ConclusionsWe obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

365

207

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Awareness without Neural Networks: Achieving Self-Aware AI via Evolutionary and Adversarial Processes

Greenwood¹,

Sundaram²,

Muirhead³

et al. 2020

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)

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Artificial Intelligence Methodologies and Their Application to Diabetes

Rigla¹,

García-Sáez

Pons³

et al. 2017

J Diabetes Sci Technol

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In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.

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A Computational Proof of Concept of a Machine-Intelligent Artificial Pancreas Using Lyapunov Stability and Differential Game Theory

Abstract: These results suggest that this is a stable, high-confidence way to generate closed-loop insulin infusion strategies.

Cited by 4 publications

References 38 publications

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Awareness without Neural Networks: Achieving Self-Aware AI via Evolutionary and Adversarial Processes

Artificial Intelligence Methodologies and Their Application to Diabetes

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