TY - JOUR
T1 - Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes
T2 - Best Practices, Pitfalls, and Opportunities
AU - Jacobs, Peter G.
AU - Herrero, Pau
AU - Facchinetti, Andrea
AU - Vehi, Josep
AU - Kovatchev, Boris
AU - Breton, Marc D.
AU - Cinar, Ali
AU - Nikita, Konstantina S.
AU - Doyle, Francis J.
AU - Bondia, Jorge
AU - Battelino, Tadej
AU - Castle, Jessica R.
AU - Zarkogianni, Konstantia
AU - Narayan, Rahul
AU - Mosquera-Lopez, Clara
N1 - Publisher Copyright:
© 2008-2011 IEEE.
PY - 2024
Y1 - 2024
N2 - 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 open-source 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.
AB - 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 open-source 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.
KW - Diabetes
KW - artificial intelligence
KW - automated insulin delivery
KW - data science
KW - decision support
KW - deep learning
KW - feature engineering
KW - glucose prediction
KW - machine learning
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U2 - 10.1109/RBME.2023.3331297
DO - 10.1109/RBME.2023.3331297
M3 - Article
C2 - 37943654
AN - SCOPUS:85177029150
SN - 1937-3333
VL - 17
SP - 19
EP - 41
JO - IEEE Reviews in Biomedical Engineering
JF - IEEE Reviews in Biomedical Engineering
ER -