Key result
A review of 57 papers revealed a categorical distinction between hypoglycemia prediction and detection, highlighting various machine learning approaches, training data types, and prediction horizons.
Why the study?
Severe hypoglycemia often occurs asymptomatically and can lead to serious complications or death in diabetic patients, making prediction vital.
Population
57 shortlisted papers on hypoglycemia prediction using machine learning
Design
Literature review
Authors
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Clinicians should distinguish prediction from detection models when evaluating ML hypoglycemia tools; extends literature mapping but leaves clinical validation open.
This review provides a comprehensive overview of machine learning approaches used for predicting and detecting hypoglycemia in diabetic patients.
Mujahid et al. (2021) conducted a review in Hypoglycemia in diabetic patients (n=57). Machine learning techniques was evaluated on Hypoglycemia prediction and detection. A review of 57 papers revealed a categorical distinction between hypoglycemia prediction and detection, highlighting various machine learning approaches, training data types, and prediction horizons.
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