Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes.

Vera LehmannThomas ZuegerMartin MaritschMathias KrausCaroline AlbrechtCaterina BérubéStefan FeuerriegelFelix WortmannTobias KowatschNaïma StygerSophie LaggerMarkus LaimerElgar FleischChristoph Stettler

Published in: Diabetes, obesity & metabolism (2023)

Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia.

Keyphrases

type diabetes
machine learning
loop mediated isothermal amplification
real time pcr
cardiovascular disease
label free
glycemic control
electronic health record
insulin resistance
deep learning
metabolic syndrome
data analysis