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

Lehmann V, Zueger T, Maritsch M, Kraus M, Albrecht C, Berube C, Feuerriegel S, Wortmann F, Kowatsch T, Styger N, Lagger S, Laimer M, Fleisch E, Stettler C (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Diabetes Obesity & Metabolism Wiley-Blackwell

DOI: 10.1111/dom.15021

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.

Authors with CRIS profile

Mathias Kraus

Involved external institutions

Universität Bern CH Switzerland (CH) Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich CH Switzerland (CH) Ludwig-Maximilians-Universität (LMU) DE Germany (DE) Universität St.Gallen CH Switzerland (CH)

How to cite

APA:

Lehmann, V., Zueger, T., Maritsch, M., Kraus, M., Albrecht, C., Berube, C.,... Stettler, C. (2023). Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes. Diabetes Obesity & Metabolism. https://dx.doi.org/10.1111/dom.15021

MLA:

Lehmann, Vera, et al. "Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes." Diabetes Obesity & Metabolism (2023).

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