Peter G. Jacobs,Pau Herrero,Andrea Facchinetti,Josep Vehı́,Boris Kovatchev,Marc D. Breton,Ali Çınar,Konstantina S. Nikita,Francis J. Doyle,Jorge Bondía,Tadej Battelino,Jessica R. Castle,Konstantia Zarkogianni,Rahul Narayan,Clara Mosquera-Lopez
出处
期刊:IEEE Reviews in Biomedical Engineering [Institute of Electrical and Electronics Engineers] 日期:2023-11-09卷期号:17: 19-41被引量:17
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.