Shilo, S. et al. 10 K: a large-scale prospective longitudinal study in Israel. Eur. J. Epidemiol. 36, 1187–1194 (2021).
Google Scholar
Reicher, L. et al. Deep phenotyping of health–disease continuum in the Human Phenotype Project. Nat. Med. 31, 3191–3203 (2025).
Google Scholar
Nathan, D. M. et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329, 977–986 (1993).
Google Scholar
King, P., Peacock, I. & Donnelly, R. The UK prospective diabetes study (UKPDS): clinical and therapeutic implications for type 2 diabetes. Br. J. Clin. Pharmacol. 48, 643–648 (1999).
Google Scholar
Gude, F. et al. Glycemic variability and its association with demographics and lifestyles in a general adult population. J. Diabetes Sci. Technol. 11, 780–790 (2017).
Google Scholar
Saab, K. et al. Capabilities of Gemini models in medicine. Preprint at https://doi.org/10.48550/arxiv.2404.18416 (2024).
Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–163 (2023).
Google Scholar
Lutsker, G., Rossman, H., Godiva, N. & Segal, E. COMPRER: a multimodal multi-objective pretraining framework for enhanced medical image representation. Preprint at https://doi.org/10.48550/arxiv.2403.09672 (2024).
Yuan, H. et al. Self-supervised learning for human activity recognition using 700,000 person-days of wearable data. npj Digit. Med. 7, 91 (2024).
Google Scholar
Thapa, R. et al. SleepFM: multi-modal representation learning for sleep across brain activity, ECG and respiratory signals. Proc. Mach. Learn. Res. 235, 48019–48037 (2024).
Chen, R. J. et al. Towards a general-purpose foundation model for computational pathology. Nat. Med. 30, 850–862 (2024).
Google Scholar
Krishnan, R., Rajpurkar, P. & Topol, E. J. Self-supervised learning in medicine and healthcare. Nat. Biomed. Eng. 6, 1346–1352 (2022).
Google Scholar
GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 402, 203–234 (2023).
Google Scholar
Rawshani, A. et al. Mortality and cardiovascular disease in type 1 and type 2 diabetes. N. Engl. J. Med. 376, 1407–1418 (2017).
Google Scholar
Moser, E. G., Crew, L. B. & Garg, S. K. Role of continuous glucose monitoring in diabetes management. Av. Diabetol. 26, 73–78 (2010).
Google Scholar
Battelino, T. et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 11, 42–57 (2023).
Google Scholar
Kieu, A., King, J., Govender, R. D. & Östlundh, L. The benefits of utilizing continuous glucose monitoring of diabetes mellitus in primary care: a systematic review. J. Diabetes Sci. Technol. 17, 762–774 (2023).
Google Scholar
Holzer, R., Bloch, W. & Brinkmann, C. Continuous glucose monitoring in healthy adults-possible applications in health care, wellness, and sports. Sensors 22, 2030 (2022).
Google Scholar
Zahedani, A. D. et al. Digital health application integrating wearable data and behavioral patterns improves metabolic health. npj Digit. Med. 6, 216 (2023).
Google Scholar
Shilo, S. et al. Continuous glucose monitoring and intrapersonal variability in fasting glucose. Nat. Med. 30, 1424–1431 (2024).
Google Scholar
U.S. Food & Drug Administration. FDA clears first over-the-counter continuous glucose monitor. FDA https://www.fda.gov/news-events/press-announcements/fda-clears-first-over-counter-continuous-glucose-monitor (2024).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://doi.org/10.48550/arxiv.1802.03426 (2018).
Bergenstal, R. M. et al. Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring. Diabetes Care 41, 2275–2280 (2018).
Google Scholar
Broll, S. et al. Interpreting blood GLUcose data with R package iglu. PLoS ONE 16, e0248560 (2021).
Google Scholar
Rodbard, D. New and improved methods to characterize glycemic variability using continuous glucose monitoring. Diabetes Technol. Ther. 11, 551–565 (2009).
Google Scholar
Wang, J. et al. Self-improving generative foundation model for synthetic medical image generation and clinical applications. Nat. Med. 31, 609–617 (2025).
Google Scholar
Keshet, A. et al. CGMap: characterizing continuous glucose monitor data in thousands of non-diabetic individuals. Cell Metab. 35, 758–769 (2023).
Google Scholar
Vickers, A. J., Van Calster, B. & Steyerberg, E. W. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 352, i6 (2016).
Google Scholar
Van Calster, B. et al. Performance evaluation of predictive AI models to support medical decisions: overview and guidance. Preprint at https://doi.org/10.48550/arxiv.2412.10288 (2024).
Htet, T. D. et al. Rationale and design of a randomised controlled trial testing the effect of personalised diet in individuals with pre-diabetes or type 2 diabetes mellitus treated with metformin. BMJ Open 10, e037859 (2020).
Google Scholar
Rein, M. S. et al. BREAst Cancer Personalised NuTrition (BREACPNT): dietary intervention in breast cancer survivors treated with endocrine therapy—a protocol for a randomised clinical trial. BMJ Open 12, e062498 (2022).
Google Scholar
Ben-Yacov, O. et al. Personalized postprandial glucose response-targeting diet versus Mediterranean diet for glycemic control in prediabetes. Diabetes Care 44, 1980–1991 (2021).
Google Scholar
The Diabetes Prevention Program Research Group. The Diabetes Prevention Program. Design and methods for a clinical trial in the prevention of type 2 diabetes. Diabetes Care 22, 623–634 (1999).
Google Scholar
International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 32, 1327–1334 (2009).
Google Scholar
Cersosimo, E., Solis-Herrera, C., Trautmann, M. E., Malloy, J. & Triplitt, C. L. Assessment of pancreatic β-cell function: review of methods and clinical applications. Curr. Diabetes Rev. 10, 2–42 (2014).
Google Scholar
Abdul-Ghani, M. A. et al. The relationship between fasting hyperglycemia and insulin secretion in subjects with normal or impaired glucose tolerance. Am. J. Physiol. Endocrinol. Metab. 295, E401–E406 (2008).
Google Scholar
Ansari, A. F. et al. Chronos: learning the language of time series. Transact. Mach. Learn. Res. https://openreview.net/forum?id=gerNCVqqtR (2024).
Rabanser, S., Januschowski, T., Flunkert, V., Salinas, D. & Gasthaus, J. The effectiveness of discretization in forecasting: an empirical study on neural time series models. Preprint at https://doi.org/10.48550/arxiv.2005.10111 (2020).
van den Oord, A. et al. WaveNet: a generative model for raw audio. In Proc. 9th ISCA Speech Synthesis Workshop 125 (2016).
van den Oord, A., Kalchbrenner, N. & Kavukcuoglu, K. Pixel recurrent neural networks. In Proc. 33rd International Conference on Machine Learning (eds Balcan, M. F. & Weinberger, K. Q.) 1747–1756 (2016).
Danne, T. et al. International consensus on use of continuous glucose monitoring. Diabetes Care 40, 1631–1640 (2017).
Google Scholar
Cefalu, W. T. et al. A global initiative to deliver precision health in diabetes. Nat. Med. 30, 1819–1822 (2024).
Google Scholar
Ahlqvist, E., Prasad, R. B. & Groop, L. Subtypes of type 2 diabetes determined from clinical parameters. Diabetes 69, 2086–2093 (2020).
Google Scholar
Xiong, Z. et al. How generalizable are foundation models when applied to different demographic groups and settings? NEJM AI https://doi.org/10.1056/AIcs2400497 (2024).
Vaswani, A. et al. Attention is all you need. In Adv. Neural Information Processing Systems (eds Guyon, I. et al.) 30, 5998–6008 (2017).
Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. In Proc. 7th International Conference on Learning Representations https://openreview.net/forum?id=Bkg6RiCqY7 (2019).
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In Proc. 37th International Conference on Machine Learning (eds Daumé, H. D. III & Singh, A.) 119, 1597–1607 (2020).
He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 9729–9738 (2020).
Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning (eds. Meila, M. & Zhang, T.) 139, 8748–8763 (2021).
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds. Burstein, J. et al.) Vol. 1 (Long and Short Papers), 4171–4186 (2019).
Yuan, H. et al. Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality. npj Digit. Med. 7, 86 (2024).
■ مصدر الخبر الأصلي
نشر لأول مرة على: www.nature.com
تاريخ النشر: 2026-01-14 02:00:00
الكاتب: Guy Lutsker
تنويه من موقع “yalebnan.org”:
تم جلب هذا المحتوى بشكل آلي من المصدر:
www.nature.com
بتاريخ: 2026-01-14 02:00:00.
الآراء والمعلومات الواردة في هذا المقال لا تعبر بالضرورة عن رأي موقع “yalebnan.org”، والمسؤولية الكاملة تقع على عاتق المصدر الأصلي.
ملاحظة: قد يتم استخدام الترجمة الآلية في بعض الأحيان لتوفير هذا المحتوى.
