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Kaizer, J. S., Heller, A. K. & Oberkampf, W. L. Scientific computer simulation review. Reliab. Eng. Syst. Saf. 138, 210218 (2015).

Article Google Scholar

Kadota, R. et al. A mathematical model of type 1 diabetes involving leptin effects on glucose metabolism. J. Theor. Biol. 456, 213223 (2018).

Article MathSciNet CAS PubMed ADS Google Scholar

Farmer Jr, T., Edgar, T. & Peppas, N. Pharmacokinetic modeling of the glucoregulatory system. J. Drug Deliv. Sci. Technol. 18, 387 (2008).

Article CAS PubMed Google Scholar

Nath, A., Biradar, S., Balan, A., Dey, R. & Padhi, R. Physiological models and control for type 1 diabetes mellitus: a brief review. IFAC-PapersOnLine 51, 289294 (2018).

Article Google Scholar

Mansell, E. J., Docherty, P. D. & Chase, J. G. Shedding light on grey noise in diabetes modelling. Biomed. Signal Process. Control 31, 1630 (2017).

Article Google Scholar

Mari, A., Tura, A., Grespan, E. & Bizzotto, R. Mathematical modeling for the physiological and clinical investigation of glucose homeostasis and diabetes. Front. Physiol. https://doi.org/10.3389/fphys.2020.575789 (2020).

Hovorka, R. et al. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol. Meas. 25, 905 (2004).

Article PubMed Google Scholar

Man, C. D. et al. The UVA/PADOVA type 1 diabetes simulator: new features. J. Diabetes Sci. Technol. 8, 2634 (2014).

Article PubMed PubMed Central Google Scholar

Bergman, R. N. & Urquhart, J. The pilot gland approach to the study of insulin secretory dynamics. In Proceedings of the 1970 Laurentian Hormone Conference 583605 (Elsevier, 1971).

Franco, R. et al. Output-feedback sliding-mode controller for blood glucose regulation in critically ill patients affected by type 1 diabetes. IEEE Trans. Control Syst. Technol. 29, 27042711 (2021).

Article Google Scholar

Nielsen, M. A visual proof that neural nets can compute any function. http://neuralnetworksanddeeplearning.com/chap4.html (2016).

Zhou, D.-X. Universality of deep convolutional neural networks. Appl. Comput. Harmon. Anal. 48, 787794 (2020).

Article MathSciNet Google Scholar

Nikzad, M., Movagharnejad, K., Talebnia, F. Comparative study between neural network model and mathematical models for prediction of glucose concentration during enzymatic hydrolysis. Int. J. Comput. Appl. 56, 1 (2012).

Nalisnick, E.T., Matsukawa, A., Teh, Y.W., Grr, D., Lakshminarayanan, B.: Do deep generative models know what they dont know? In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, https://openreview.net/forum?id=H1xwNhCcYm (2019).

Noguer, J., Contreras, I., Mujahid, O., Beneyto, A. & Vehi, J. Generation of individualized synthetic data for augmentation of the type 1 diabetes data sets using deep learning models. Sensors. https://doi.org/10.3390/s22134944 (2022).

Thambawita, V. et al. Deepfake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine. Sci. Rep. 11, 18 (2021).

Article Google Scholar

Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11, 112 (2020).

Article Google Scholar

Festag, S., Denzler, J. & Spreckelsen, C. Generative adversarial networks for biomedical time series forecasting and imputation. J. Biomed. Inform. 129, 104058 (2022).

Article PubMed Google Scholar

Xu, J., Li, H. & Zhou, S. An overview of deep generative models. IETE Tech. Rev. 32, 131139 (2015).

Article Google Scholar

Wan, C. & Jones, D. T. Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks. Nat. Mach. Intell. 2, 540550 (2020).

Article Google Scholar

Choudhury, S., Moret, M., Salvy, P., Weilandt, D., Hatzimanikatis, V., & Miskovic, L. Reconstructing kinetic models for dynamical studies of metabolism using generative adversarial networks. Nat. Mach. Intell. 4, 710719 (2022).

Dieng, A.B., Kim, Y., Rush, A. M. & Blei, D. M. Avoiding latent variable collapse with generative skip models. In Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research (eds Chaudhuri, K. & Sugiyama, M.) Vol. 89, 23972405 (PMLR, 2019).

Ruthotto, L. & Haber, E. An introduction to deep generative modeling. GAMM-Mitteilungen 44, 202100008 (2021).

Article MathSciNet Google Scholar

Xie, T. et al. Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis. Comput. Biol. Med. 159, 106947 (2023).

Article CAS PubMed PubMed Central Google Scholar

Li, H., Zeng, N., Wu, P. & Clawson, K. Cov-net: A computer-aided diagnosis method for recognizing COVID-19 from chest x-ray images via machine vision. Expert Syst. Appl. 207, 118029 (2022).

Article PubMed PubMed Central Google Scholar

Li, K., Liu, C., Zhu, T., Herrero, P. & Georgiou, P. Glunet: a deep learning framework for accurate glucose forecasting. IEEE J. Biomed. health Inform. 24, 414423 (2019).

Article PubMed Google Scholar

Rabby, M. F. et al. Stacked LSTM based deep recurrent neural network with Kalman smoothing for blood glucose prediction. BMC Med. Inform. Decis. Mak. 21, 115 (2021).

Article Google Scholar

Munoz-Organero, M. Deep physiological model for blood glucose prediction in T1DM patients. Sensors 20, 3896 (2020).

Article CAS PubMed PubMed Central ADS Google Scholar

Noaro, G., Zhu, T., Cappon, G., Facchinetti, A. & Georgiou, P. A personalized and adaptive insulin bolus calculator based on double deep q-learning to improve type 1 diabetes management. IEEE J. Biomed. Health Inform. 27, pp. 25362544 (2023).

Emerson, H., Guy, M. & McConville, R. Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes. J. Biomed. Inform. 142, 104376 (2023).

Article PubMed Google Scholar

Lemercier, J.-M., Richter, J., Welker, S. & Gerkmann, T. Analysing diffusion-based generative approaches versus discriminative approaches for speech restoration. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 15 (2023).

Richter, J., Welker, S., Lemercier, J.-M., Lay, B. & Gerkmann, T. Speech enhancement and dereverberation with diffusion-based generative models. In IEEE/ACM Transactions on Audio, Speech, and Language Processing 113 (2023).

Yoo, T. K. et al. Deep learning can generate traditional retinal fundus photographs using ultra-widefield images via generative adversarial networks. Comput. Methods Prog. Biomed. 197, 105761 (2020).

Article Google Scholar

You, A., Kim, J. K., Ryu, I. H. & Yoo, T. K. Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye Vis. 9, 119 (2022).

Article Google Scholar

Liu, M. et al. Aa-wgan: attention augmented Wasserstein generative adversarial network with application to fundus retinal vessel segmentation. Comput. Biol. Med. 158, 106874 (2023).

Article PubMed Google Scholar

Wang, S. et al. Diabetic retinopathy diagnosis using multichannel generative adversarial network with semisupervision. IEEE Trans. Autom. Sci. Eng. 18, 574585 (2021).

Article Google Scholar

Zhou, Y., Wang, B., He, X., Cui, S. & Shao, L. DR-GAN: conditional generative adversarial network for fine-grained lesion synthesis on diabetic retinopathy images. IEEE J. Biomed. Health Inform. 26, 5666 (2020).

Article CAS Google Scholar

Liu, S. et al. Prediction of OCT images of short-term response to anti-VEGF treatment for diabetic macular edema using different generative adversarial networks. Photodiagnosis Photodyn. Ther. 41, 103272 (2023).

Sun, L.-C. et al. Generative adversarial network-based deep learning approach in classification of retinal conditions with optical coherence tomography images. Graefes Arch. Clin. Exp. Ophthalmol. 261, 13991412 (2023).

Article Google Scholar

Zhang, J., Zhu, E., Guo, X., Chen, H. & Yin, J. Chronic wounds image generator based on deep convolutional generative adversarial networks. In Theoretical Computer Science: 36th National Conference, NCTCS 2018, Shanghai, China, October 1314, 2018, Proceedings 36, 150158 (Springer, 2018).

Cichosz, S. L. & Xylander, A. A. P. A conditional generative adversarial network for synthesis of continuous glucose monitoring signals. J. Diabetes Sci. Technol. 16, 12201223 (2022).

Article PubMed Google Scholar

Mujahid, O. et al. Conditional synthesis of blood glucose profiles for T1D patients using deep generative models. Mathematics. https://doi.org/10.3390/math10203741 (2022).

Eunice, H. W. & Hargreaves, C. A. Simulation of synthetic diabetes tabular data using generative adversarial networks. Clin. Med. J. 7, 4959 (2021).

Che, Z., Cheng, Y., Zhai, S., Sun, Z. & Liu, Y. Boosting deep learning risk prediction with generative adversarial networks for electronic health records. In 2017 IEEE International Conference on Data Mining (ICDM) 787792 (2017).

Noguer, J., Contreras, I., Mujahid, O., Beneyto, A. & Vehi, J. Generation of individualized synthetic data for augmentation of the type 1 diabetes data sets using deep learning models. Sensors 22, 4944 (2022).

Article CAS PubMed PubMed Central ADS Google Scholar

Lim, G., Thombre, P., Lee, M. L. & Hsu, W. Generative data augmentation for diabetic retinopathy classification. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI) 10961103 (2020).

Zhu, T., Yao, X., Li, K., Herrero, P. & Georgiou, P. Blood glucose prediction for type 1 diabetes using generative adversarial networks. In CEUR Workshop Proceedings, Vol. 2675, 9094 (2020).

Zeng, A., Chen, M., Zhang, L., & Xu, Q. Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence.37, pp. 1112111128 (2023).

Zhu, T., Li, K., Herrero, P. & Georgiou, P. Glugan: generating personalized glucose time series using generative adversarial networks. IEEE J. Biomed. Health Inf. https://doi.org/10.1109/JBHI.2023.3271615 (2023).

Lanusse, F. et al. Deep generative models for galaxy image simulations. Mon. Not. R. Astron. Soc. 504, 55435555 (2021).

Article ADS Google Scholar

Ghosh, A. & ATLAS collaboration. Deep generative models for fast shower simulation in ATLAS. In Journal of Physics: Conference Series. IOP Publishing. 1525, p. 012077 (2020).

Borsoi, R. A., Imbiriba, T. & Bermudez, J. C. M. Deep generative endmember modeling: an application to unsupervised spectral unmixing. IEEE Trans. Comput. Imaging 6, 374384 (2019).

Article MathSciNet Google Scholar

Ma, H., Bhowmik, D., Lee, H., Turilli, M., Young, M., Jha, S., & Ramanathan, A.. Deep generative model driven protein folding simulations. In I. Foster, G. R. Joubert, L. Kucera, W. E. Nagel, & F. Peters (Eds.), Parallel Computing: Technology Trends (pp. 4555). (Advances in Parallel Computing; Vol. 36). IOS Press BV. https://doi.org/10.3233/APC200023 (2020)

Wen, J., Ma, H. & Luo, X. Deep generative smoke simulator: connecting simulated and real data. Vis. Comput. 36, 13851399 (2020).

Article Google Scholar

Mincu, D. & Roy, S. Developing robust benchmarks for driving forward AI innovation in healthcare. Nat. Mach. Intell. 4, 916921 (2022).

Mirza, M. & Osindero, S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).

Isola, P., Zhu, J.-Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 11251134 (2017).

Ahmad, S. et al. Generation of virtual patient populations that represent real type 1 diabetes cohorts. Mathematics 9, 1200 (2021).

Bertachi, A. et al. Prediction of nocturnal hypoglycemia in adults with type 1 diabetes under multiple daily injections using continuous glucose monitoring and physical activity monitor. Sensors https://doi.org/10.3390/s20061705 (2020).

Marling, C. & Bunescu, R. The OhioT1DM dataset for blood glucose level prediction: update 2020. In CEUR Workshop Proceedings, Vol. 2675, 71 (NIH Public Access, 2020).

Estremera, E., Cabrera, A., Beneyto, A. & Vehi, J. A simulator with realistic and challenging scenarios for virtual T1D patients undergoing CSII and MDI therapy. J. Biomed. Inform. 132, 104141 (2022).

Article PubMed Google Scholar

Marin, I., Gotovac, S., Russo, M. & Boi-tuli, D. The effect of latent space dimension on the quality of synthesized human face images. J. Commun. Softw. Syst. 17, 124133 (2021).

Article Google Scholar

The Editorial Board. Into the latent space. Nat. Mach. Intell. 2, 151 (2020).

Battelino, T. et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. https://doi.org/10.1016/S2213-8587(22)00319-9 (2022).

Beneyto, A., Bertachi, A., Bondia, J. & Vehi, J. A new blood glucose control scheme for unannounced exercise in type 1 diabetic subjects. IEEE Trans. Control Syst. Technol. 28, 593600 (2020).

Article Google Scholar

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