연속 혈당 데이터로 당뇨 전단계를 95% 정확도로 예측할 수 있을까요?

연속 혈당 측정 데이터와 머신러닝 알고리즘을 활용한 당뇨병 전단계 예측 기술의 핵심 원리와 정확도를 설명합니다.

📚 참고 자료 및 근거
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[1] Links between physical activity, time-in-range and glucose predictability in people with type 1 diabetes. | Saavedra MD, Inthamoussou FA, Fushimi E et al. (2026)
https://pubmed.ncbi.nlm.nih.gov/41924857/

[2] Enhanced diabetes prediction using pre-trained CNNs, LSTM, and conditional GAN on transformed numerical data. | Singh KR, Dash S, Liu H et al. (2026)
→ Diabetes remains a major public health challenge, contributing to complications such as kidney disease, cardiovascular disorders, and diabetic retinopathy. Early detection is essential for timely intervention, yet prediction from structured
https://pubmed.ncbi.nlm.nih.gov/41667617/

[3] Personalized non-invasive continuous glucose monitoring via multiparameter-informed machine learning. | Zhu W, Li X, Hou J et al. (2026)
→ Non-invasive continuous glucose monitoring (NCGM) based on reverse iontophoresis (RI) holds considerable promise for diabetes management; however, its clinical translation is constrained by limited predictive accuracy. This limitation prima
https://pubmed.ncbi.nlm.nih.gov/42217397/

[4] Temporal and Behaviour-Aware Multimodal Modelling for Hour-Ahead Hypoglycaemia Prediction During Ramadan Fasting in Type 1 Diabetes. | Alkhateeb M, AlSaad R, Brahim Belhaouari S et al. (2026)
→ Ramadan fasting substantially alters meal timing, sleep patterns, and daily activity, thereby increasing the risk of hypoglycaemia in adults with type 1 diabetes (T1D). Although continuous glucose monitoring (CGM) systems provide real-time
https://pubmed.ncbi.nlm.nih.gov/42076661/

[5] AFTS: A patient-agnostic encoder-decoder architecture with directional attention for blood glucose forecasting. | Chen Y, Lin H, Wang Z et al. (2026)
→ Accurate blood glucose forecasting remains challenging due to inter-patient heterogeneity and complex glycemic dynamics. We present AFTS (Adaptive Feature Time Series), a patient-agnostic deep learning architecture combining a bidirectional
https://pubmed.ncbi.nlm.nih.gov/41687901/
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Видео 연속 혈당 데이터로 당뇨 전단계를 95% 정확도로 예측할 수 있을까요? канала Aging2042