Artificial intelligence and machine learning for improving glycemic control in diabetes: best practices, pitfalls and opportunities

Peter G. Jacobs*, Pau Herrero, Andrea Facchinetti, Josep Vehi, Boris Kovatchev, Marc Breton, Ali Cinar, Konstantina S. Nikita, Frank Doyle, Jorge Bondia, Tatej Battelino, Jessica R. Castle, Konstantia Zarkogianni, Rahul Narayan, Clara Mosquera-Lopez

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)19-41
Number of pages23
JournalIEEE Reviews in Biomedical Engineering
Volume17
Early online date2023
DOIs
Publication statusPublished - 2024

Keywords

  • Diabetes
  • artificial intelligence
  • automated insulin delivery
  • data science
  • decision support
  • deep learning
  • feature engineering
  • glusoce predicition
  • machine learning

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