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

Diabetes Obes Metab. 2023 Jun;25(6):1668-1676. doi: 10.1111/dom.15021. Epub 2023 Mar 6.

Abstract

Aim: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data.

Materials and methods: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced hypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1 ). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1 ).

Results: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively).

Conclusions: 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.

Keywords: diabetes complications; glycaemic control; hypoglycaemia; type 1 diabetes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Blood Glucose
  • Diabetes Mellitus, Type 1* / complications
  • Diabetes Mellitus, Type 1* / diagnosis
  • Humans
  • Hypoglycemia* / chemically induced
  • Hypoglycemia* / diagnosis
  • Insulin / adverse effects

Substances

  • Blood Glucose
  • Insulin