Ewha Medical Journal

"Blood glucose"

Original article

[English]

Cyclic dual latent discovery for improved blood glucose prediction through patient–provider interaction modeling: a prediction study: Suyeon Park, Seoyoung Kim, Dohyoung Rim; Ewha Med J 2025;48(2):e34. Published online April 15, 2025; DOI: https://doi.org/10.12771/emj.2025.00332

Purpose
Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy.
Methods
ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers.
Results
CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches.
Conclusion
Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

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Review Article

[Korean]

Clinical Utilities of Continuous Glucose Monitoring and Insulin Pumps in Pediatric Patients with Type 1 Diabetes: Ewha Med J 2021;44(3):55-62. Published online July 31, 2021; DOI: https://doi.org/10.12771/emj.2021.44.3.55

Abstract
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Type 1 diabetes requires lifelong insulin therapy because insulin-secretion capability is diminished. Glycemic control and glucose monitoring are important to prevent type 1 diabetes complications. Diabetes technologies have developed rapidly; continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) are now common and greatly aid glycemic control, especially in children and adolescents. The National Health Insurance Service has provided partial reimbursements for both CGM and CSII devices since 2019 and 2020, respectively; the devices are thus expected to become more popular. CGM reduces the frequency of hypoglycemia and the level of glycated hemoglobin. CSII affords more precise glycemic control than multi-dose insulin therapy. CSII showed reduced frequency of hypoglycemia and improved metabolic outcome without an increase in the body mass index z-score. Technological advancement of combined CGM and CSII will eventually serve as an artificial pancreas. The National Health Insurance Service should fund not only the devices but also education of patients and caregivers. In addition, healthcare providers must be continuously updated on new diabetes technologies.

Citations

Citations to this article as recorded by

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