Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s12204-022-2439-0.pdf
Reference32 articles.
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2. CHARLEER S, DE BLOCK C, VAN HUFFEL L, et al. Quality of life and glucose control after 1 year of nationwide reimbursement of intermittently scanned continuous glucose monitoring in adults living with type 1 diabetes (FUTURE): A prospective observational real-world cohort study [J]. Diabetes Care, 2020, 43(2): 389–397.
3. MONNIER L, COLETTE C, OWENS D R. The application of simple metrics in the assessment of glycaemic variability [J]. Diabetes & Metabolism, 2018, 44(4): 313–319.
4. LISZKA-HACKZELL J J. Prediction of blood glucose levels in diabetic patients using a hybrid AI technique [J]. Computers and Biomedical Research, 1999, 32(2): 132–144.
5. CHUAH Z M, PARAMESRAN R, THAMBIRATNAM K, et al. A two-level partial least squares system for non-invasive blood glucose concentration prediction [J]. Chemometrics and Intelligent Laboratory Systems, 2010, 104(2): 347–351.
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