Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras, Iván; Vehı́, Josep

doi:10.2196/10775

Cited by 363 publications

(239 citation statements)

References 141 publications

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“…Over the last few decades, machine‐learning algorithms have been actively used for developing clinical decision support systems . Machine‐learning algorithms can automatically identify important clinical variables to predict a clinical outcome at individual patient level.…”

Section: Introductionmentioning

confidence: 99%

“…Over the last few decades, machine-learning algorithms have been actively used for developing clinical decision support systems. 19,20 Machine-learning algorithms can automatically identify important clinical variables to predict a clinical outcome at individual patient level. In the present study, our primary aims were to assess the performance of supervised machine-learning-based clinical decision support tools to predict short-and long-term HbA1c response after insulin initiation in patients with T2DM and to identify clinical variables that can influence a patient's HbA1c response.…”

Section: Introductionmentioning

confidence: 99%

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Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms

Nagaraj

Sidorenkov

Boven

et al. 2019

Diabetes Obesity Metabolism

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Aim To assess the potential of supervised machine‐learning techniques to identify clinical variables for predicting short‐term and long‐term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM). Materials and methods We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow‐up of 2 years. Short‐ and long‐term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty‐four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine‐learning algorithms was compared for the prediction of short‐ and long‐term responses and the area under the receiver‐operating characteristic curve (AUC) was used to assess the performance of the prediction models. Results The elastic net regularization‐based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short‐ and long‐term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78–0.83) and 0.81 (95% CI 0.79–0.84), respectively, and outperformed the other machine‐learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65–0.73) and 0.72 (95% CI 0.66–0.75) was obtained for predicting short‐term and long‐term response, respectively. Conclusions Machine‐learning algorithm performed well in the prediction of an individual's short‐term and long‐term HbA1c response using baseline clinical variables.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms

Nagaraj

Sidorenkov

Boven

et al. 2019

Diabetes Obesity Metabolism

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show abstract

“…7) Alternative Control Methods: While clinical studies have been dominated by PID, MPC, and Fuzzy-Logic controllers (with several using CTR), a variety of additional paradigms have been studied in academic simulations. These methods include artificial intelligence methods [152], H∞ control [55], linear parameter varying (LPV) control [77], and sliding mode control [153]. However, recent clinical studies implementing these techniques are sparse, if available at all.…”

Section: Automated Therapy For Diabetesmentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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“…Work is being done on physician-focused ADS systems to help providers identify whom to screen for T2D, how to optimize complication reduction and guideline compliance with routine studies for patients with T1D and T2D, and automated reading of images screening for diabetic retinopathy. [19][20][21][22] Although these frontiers are exciting, they are more focused on provider efficiency rather than patient-centered application of ADS technologies. To date, there are limited clinical trial data on the use of ADS for MDI patients.…”

Section: Studies On Ads Technologymentioning

confidence: 99%

Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy

Forlenza

2019

Diabetes Technology & Therapeutics

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Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Cited by 363 publications

References 141 publications

Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms

Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy

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