Behavior monitoring under uncertainty using Bayesian surprise and optimal action selection

Ávila, Luis; Martínez, Ernesto

doi:10.1016/j.eswa.2014.04.031

Cited by 7 publications

(6 citation statements)

References 49 publications

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“…It is also common to find some reference values related to normal, hyper and hypoglycemia ranges in order to establish good rewards and penalties. However, we found that only five papers include the actions taken in the reward function [31,32,38,42,44]. We think it could be interesting to also consider the insulin doses in the reward function, which for example can lead to take less aggressive actions for the patients.…”

Section: Discussionmentioning

confidence: 99%

Reinforcement learning application in diabetes blood glucose control: A systematic review

Tejedor

Woldaregay

Godtliebsen

2020

Artificial Intelligence in Medicine

119

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Background: Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. It is designed for problems which include a learning agent interacting with its environment to achieve a goal. For example, blood glucose (BG) control in diabetes mellitus (DM), where the learning agent and its environment are the controller and the body of the patient respectively. RL algorithms could be used to design a fully closed-loop controller, providing a truly personalized insulin dosage regimen based exclusively on the patient's own data. Objective:In this review we aim to evaluate state-of-the-art RL approaches to designing BG control algorithms in DM patients, reporting successfully implemented RL algorithms in closedloop, insulin infusion, decision support and personalized feedback in the context of DM.Methods: An exhaustive literature search was performed using different online databases, analyzing the literature from 1990 to 2019. In a first stage, a set of selection criteria were established in order to select the most relevant papers according to the title, keywords and abstract. Research questions were established and answered in a second stage, using the information extracted from the articles selected during the preliminary selection.Results: The initial search using title, keywords, and abstracts resulted in a total of 404 articles.After removal of duplicates from the record, 347 articles remained. An independent analysis and screening of the records against our inclusion and exclusion criteria defined in Methods section resulted in removal of 296 articles, leaving 51 relevant articles. A full-text assessment was conducted on the remaining relevant articles, which resulted in 29 relevant articles that were critically analyzed. The inter-rater agreement was measured using Cohen Kappa test, and disagreements were resolved through discussion. Conclusions:The advances in health technologies and mobile devices have facilitated the implementation of RL algorithms for optimal glycemic regulation in diabetes. However, there exists few articles in the literature focused on the application of these algorithms to the BG regulation problem. Moreover, such algorithms are designed for control tasks as BG adjustment and their use have increased recently in the diabetes research area, therefore we foresee RL algorithms will be used more frequently for BG control in the coming years. Furthermore, in the literature there is a lack of focus on aspects that influence BG level such as meal intakes and physical activity (PA), which should be included in the control problem. Finally, there exists a need to perform clinical validation of the algorithms.

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Section: Discussionmentioning

confidence: 99%

Reinforcement learning application in diabetes blood glucose control: A systematic review

Tejedor

Woldaregay

Godtliebsen

2020

Artificial Intelligence in Medicine

119

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“…If this research is compared to other multiagent systems [43], then artificial intelligence systems (Bayesian network and expert system) are totally integrated in agents, thereby forming the fundamental part of behavior operation (to monitor the environment or to act over it). In fact, agents have no defined behavior, but their actions and monitoring tasks are always dependent on these artificial intelligence systems.…”

Section: Discussionmentioning

confidence: 99%

A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

Romero-Ternero

Oviedo-Olmedo

Carrasco

et al. 2019

Sensors

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A common problem in solar farms is to predict when accumulators stop working optimally and start losing efficiency. This paper proposes and describes how to use Bayesian networks together with expert systems to predict this moment by using a telecontrol multiagent system for monitoring solar farms with distributed sensors, which was developed in a previous work. To this end, a Bayesian network model and its implementation are proposed. The resulting system meets the requirements of telecontrol systems (reliability, flexibility, and response time), yields a solution for the prediction of lifespan batteries, and provides the multiagent system with autonomous intelligent capabilities and integrated learning.

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“…case a new specification should be recommended to the patient, it will be based a characterization of its own BG profiles (Avila & Martínez, 2014). Such profiles are obtained by employing a subcutaneous glycemic sensor, considering that the patient metabolism is stabilized at the moment of the measures.…”

Section: Predicting Specificationsmentioning

confidence: 99%

A simple method for recommending specialized specifications for diabetes monitoring

Ávila

Errecalde

2018

Expert Systems with Applications

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Behavior monitoring under uncertainty using Bayesian surprise and optimal action selection

Cited by 7 publications

References 49 publications

Reinforcement learning application in diabetes blood glucose control: A systematic review

Reinforcement learning application in diabetes blood glucose control: A systematic review

A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

A simple method for recommending specialized specifications for diabetes monitoring

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