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English Information

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Authors
# Name
1 Mario Cesar Freire(mariocesarfreire@alu.ufc.br)
2 Rodrigo Siqueira(rodrigoendocrinologista@gmail.com)
3 Vinicius Ribeiro(vinicius@osterne.com)
4 Danielo G. Gomes(danielo@ufc.br)

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Reference
# Reference
1 Alexandrino, F., Pacheco, C., Carvalho, D., and Ogasawara, E. (2024). Aumento de dados e suavização integrada para predição de séries temporais baseada em aprendizado de máquina. In Anais do XVIII Brazilian e-Science Workshop (BreSci 2024), pages 32-39. Sociedade Brasileira de Computação.
2 Ansari, A. F., Shchur, O., Küken, J., Auer, A., Han, B., Mercado, P., Rangapuram, S. S., Shen, H., Stella, L., Zhang, X., Goswami, M., Kapoor, S., Maddix, D. C., Guerron, P., Hu, T., Yin, J., Erickson, N., Desai, P. M., Wang, H., Rangwala, H., Karypis, G., Wang, Y., and Bohlke-Schneider, M. (2025). Chronos-2: From univariate to universal forecasting.
3 Bergmeir, C. and Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191:192-213.
4 Broussard, J. L., Ehrmann, D. A., Van Cauter, E., Tasali, E., and Brady, M. J. (2016). Sleep restriction impairs insulin sensitivity in human adipocytes. Diabetologia, 59:359-368.
5 Cappon, G., Vettoretti, M., Sparacino, G., and Facchinetti, A. (2019). Continuous glucose monitoring sensors for diabetes management: a review of technologies and applications. Diabetes & Metabolism Journal, 43(4):383-397.
6 Limbert, C., Kowalski, A. J., and Danne, T. P. (2024). Automated insulin delivery: A milestone on the road to insulin independence in type 1 diabetes. Diabetes Care, 47(6):918-920.
7 Marling, C. and Bunescu, R. (2020). The OhioT1DM dataset for blood glucose level prediction: Update 2020. In Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH@ECAI 2020), volume 2675 of CEUR Workshop Proceedings, pages 71-74. CEUR-WS.org.
8 Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., and Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616(7956):259-265.
9 Rancati, S., Bosoni, P., Schiaffini, R., Deodati, A., Mongini, P. A., Sacchi, L., Toffanin, C., and Bellazzi, R. (2024). Exploration of foundational models for blood glucose forecasting in type-1 diabetes pediatric patients. Diabetology, 5(6):584-599.
10 Rousseeuw, P. J. and Hubert, M. (2011). Robust statistics for outlier detection. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1):73-79.
11 Santos, M. A., Melo, Á. H. da S., Santos, J. S. dos, Gonçalves, I. C. M., Santos, W. O. M., and Cuevas R., L. (2025). Modelagem de séries temporais biomédicas: Um estudo comparativo entre PSTA-TCN e TCN Vanilla para previsão de dados de glicose de pessoas diabéticas. In Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, pages 991-996, Porto Alegre, RS, Brasil. SBC.
12 Sun, X., Li, H., and Yu, X. (2026). Future-aware blood glucose forecasting using knowledge distillation with transformer-based sequence-to-sequence models. Scientific Reports, 16:11404.
13 Turksoy, K., Bayrak, E. S., Quinn, L., Littlejohn, E., and Cinar, A. (2015). Multivariable adaptive identification and control for artificial pancreas systems. IEEE Transactions on Biomedical Engineering, 63(3):526-534.
14 Wolber, J. C., Samadi, M. E., Sellin, J., and Schuppert, A. (2025). Multimodal large language models and mechanistic modeling for glucose forecasting in type 1 diabetes patients. Journal of Biomedical Informatics, 172:104945.