Identifying Continuous Glucose Monitoring Data Using Machine Learning

Herrero, Pau; Reddy, Monika; Georgiou, Pantelis; Oliver, Nick

doi:10.1089/dia.2021.0498

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…30,31 To address similar issues, machine learning (ML)-based sweat analysis reports have been developed for the individual detection of glucose. 32,33 Autonomous ML platforms, which are expected to construct efficient and tailored medical treatment systems, can deliver high quality learning results by minimizing debugging and expert involvement.…”

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confidence: 99%

“…Another issue that needs further consideration is that because the non-invasive sweat sensor can achieve continuous in situ monitoring of biological samples, the establishment of a human health data cloud system needs to be further promoted to improve the current personalized diagnosis in medical treatment. A fixed-dose dosing approach, for example, is not the optimum choice for the treatment of PD, taking into account individual metabolic differences due to the instability of l -dopa dose and treatment duration. , It may be more advantageous for patient treatment to develop customized dose criteria in conjunction with long-term serial assessments of l -dopa levels in sweat. , To address similar issues, machine learning (ML)-based sweat analysis reports have been developed for the individual detection of glucose. , Autonomous ML platforms, which are expected to construct efficient and tailored medical treatment systems, can deliver high quality learning results by minimizing debugging and expert involvement.…”

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confidence: 99%

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Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson’s Patients Based on Vacancy-Engineered Processing of g-C₃N₄

Tang

2022

Anal. Chem.

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Herein, we developed a flexible, low-cost non-enzymatic sweat sensing chip for in situ acquisition of bioinformation in sweat of individuals under exercise conditions to advance personal health monitoring and medication management for patients with Parkinson's disease. This low-cost, flexible, wearable sweat sensor consists of a printed screen electrode modified with g-C 3 N 4 material and an external MSME element. The doping strategy and surface activation strategy of the g-C 3 N 4 -based exhibited efficient glucose oxidase-like activity and electrochemical activity when testing L-dopa and glucose in sweat. The optimized signal was transmitted to a smartphone for processing 12 individuals with simulated dosing, enabling continuous monitoring of L-dopa metabolism in sweat and management of dosing. The generalization ability and robustness of models constructed by methods such as multiple linear regression, artificial neural networks, and convolutional neural networks were compared cross-sectionally. Deep learning models based on artificial neural networks help develop a user-personalized medication administration reminder system, which provides a promising paradigm for reliable medication supervision for Parkinson's patients in the Internet of Things era.

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confidence: 99%

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confidence: 99%

Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson’s Patients Based on Vacancy-Engineered Processing of g-C₃N₄

Tang

2022

Anal. Chem.

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“…Individuals' glucose profiles change a lot day-to-day. Herrero and colleagues showed that individuals' glucose traces could uniquely identify each person thereby generating a CGM equivalent to a 'fingerprint' [154]. If individual glucose profiles have a larger inter-day variation than the variation observed in a population model, then there could be no benefit of personalization.…”

Section: F Personalized ML In Diabetes Forecasting and Treatmentmentioning

confidence: 99%

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

Jacobs,

Herrero,

Facchinetti

et al. 2024

IEEE Rev. Biomed. Eng.

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Objective: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. Methods: Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an opensource library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. Significance: These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

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“…An SVM binary classifier was developed with the purpose of determining if a CGM data stream pertains to an individual contributor [ 158 ]. To produce the variable vector employed for classification, the standard glycemic metrics were chosen and assessed at different time periods of the day (24 h, day, night, breakfast, lunch, and dinner).…”

Section: The Application Of ML and Dl Models For The Management Predi...mentioning

confidence: 99%

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Afsaneh¹,

Sharifdini²,

Ghazzaghi³

et al. 2022

Diabetol Metab Syndr

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Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

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Identifying Continuous Glucose Monitoring Data Using Machine Learning

Cited by 8 publications

References 20 publications

Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson’s Patients Based on Vacancy-Engineered Processing of g-C₃N₄

Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson’s Patients Based on Vacancy-Engineered Processing of g-C₃N₄

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

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Identifying Continuous Glucose Monitoring Data Using Machine Learning

Cited by 8 publications

References 20 publications

Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson’s Patients Based on Vacancy-Engineered Processing of g-C3N4

Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson’s Patients Based on Vacancy-Engineered Processing of g-C3N4

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

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Product

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Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson’s Patients Based on Vacancy-Engineered Processing of g-C₃N₄

Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson’s Patients Based on Vacancy-Engineered Processing of g-C₃N₄