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Lehmann, Vera; Zueger, Thomas; Maritsch, Martin ORCID logoORCID: https://orcid.org/0000-0001-9920-0587; Kraus, Mathias; Albrecht, Caroline; Bérubé, Caterina; Feuerriegel, Stefan ORCID logoORCID: https://orcid.org/0000-0001-7856-8729; Wortmann, Felix; Kowatsch, Tobias; Styger, Naïma; Lagger, Sophie; Laimer, Markus; Fleisch, Elgar and Stettler, Christoph ORCID logoORCID: https://orcid.org/0000-0003-1691-6059 (2023): Machine learning for non‐invasive sensing of hypoglycaemia while driving in people with diabetes. In: Diabetes, Obesity and Metabolism, Vol. 25, No. 6: pp. 1668-1676 [PDF, 1MB]


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.

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