Why the study?
Continuous glucose monitors are expensive and unavailable for many patients, prompting investigation into smartwatch sensors and physiological measures like heart rate variability for hypoglycemia detection.
Does a machine learning model using smartwatch sensor data accurately detect hypoglycemia in individuals with diabetes?
Does a machine learning model using smartwatch sensor data accurately detect hypoglycemia in individuals with diabetes?
A proposed machine learning model using smartwatch sensor data and SHAP values aims to detect and explain hypoglycemia events in patients with diabetes.
May aid future wearable hypoglycemia detection; leaves open prospective validation before clinical use.
Rigorous blood glucose management is vital for individuals with diabetes to prevent states of too low blood glucose (hypoglycemia). While there are continuous glucose monitors available, they are expensive and not available for many patients. Related work suggests a correlation between the blood glucose level and physiological measures, such as heart rate variability. We therefore propose a machine learning model to detect hypoglycemia on basis of data from smartwatch sensors gathered in a proof-of-concept study. In further work, we want to integrate our model in wearables and warn individuals with diabetes of possible hypoglycemia. However, presenting just the detection output alone might be confusing to a patient especially if it is a false positive result. We thus use SHAP (SHapley Additive exPlanations) values for feature attribution and a method for subsequently explaining the model decision in a comprehensible way on smartwatches.
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Maritsch et al. (2020) studied this question.
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