Kaizer, J. S., Heller, A. K. & Oberkampf, W. L. Scientific computer simulation review. Reliab. Eng. Syst. Saf. 138, 210–218 (2015).
Kadota, R. et al. A mathematical model of type 1 diabetes involving leptin effects on glucose metabolism. J. Theor. Biol. 456, 213–223 (2018).
Farmer Jr, T., Edgar, T. & Peppas, N. Pharmacokinetic modeling of the glucoregulatory system. J. Drug Deliv. Sci. Technol. 18, 387 (2008).
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, 289–294 (2018).
Mansell, E. J., Docherty, P. D. & Chase, J. G. Shedding light on grey noise in diabetes modelling. Biomed. Signal Process. Control 31, 16–30 (2017).
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).
Man, C. D. et al. The UVA/PADOVA type 1 diabetes simulator: new features. J. Diabetes Sci. Technol. 8, 26–34 (2014).
Bergman, R. N. & Urquhart, J. The pilot gland approach to the study of insulin secretory dynamics. In Proceedings of the 1970 Laurentian Hormone Conference 583–605 (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, 2704–2711 (2021).
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, 787–794 (2020).
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., Görür, D., Lakshminarayanan, B.: Do deep generative models know what they don’t 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, 1–8 (2021).
Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11, 1–12 (2020).
Festag, S., Denzler, J. & Spreckelsen, C. Generative adversarial networks for biomedical time series forecasting and imputation. J. Biomed. Inform. 129, 104058 (2022).
Xu, J., Li, H. & Zhou, S. An overview of deep generative models. IETE Tech. Rev. 32, 131–139 (2015).
Wan, C. & Jones, D. T. Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks. Nat. Mach. Intell. 2, 540–550 (2020).
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, 710–719 (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, 2397–2405 (PMLR, 2019).
Ruthotto, L. & Haber, E. An introduction to deep generative modeling. GAMM-Mitteilungen 44, 202100008 (2021).
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).
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).
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, 414–423 (2019).
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, 1–15 (2021).
Munoz-Organero, M. Deep physiological model for blood glucose prediction in T1DM patients. Sensors 20, 3896 (2020).
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. 2536–2544 (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).
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) 1–5 (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 1–13 (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).
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, 1–19 (2022).
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).
Wang, S. et al. Diabetic retinopathy diagnosis using multichannel generative adversarial network with semisupervision. IEEE Trans. Autom. Sci. Eng. 18, 574–585 (2021).
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, 56–66 (2020).
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. Graefe’s Arch. Clin. Exp. Ophthalmol. 261, 1399–1412 (2023).
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 13–14, 2018, Proceedings 36, 150–158 (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, 1220–1223 (2022).
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, 49–59 (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) 787–792 (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).
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) 1096–1103 (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, 90–94 (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. 11121–11128 (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, 5543–5555 (2021).
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, 374–384 (2019).
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. 45–55). (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, 1385–1399 (2020).
Mincu, D. & Roy, S. Developing robust benchmarks for driving forward AI innovation in healthcare. Nat. Mach. Intell. 4, 916–921 (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 1125–1134 (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).
Marin, I., Gotovac, S., Russo, M. & Božić-Štulić, D. The effect of latent space dimension on the quality of synthesized human face images. J. Commun. Softw. Syst. 17, 124–133 (2021).
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, 593–600 (2020).
Herrero, P., Alalitei, A., Reddy, M., Georgiou, P. & Oliver, N. Robust determination of the optimal continuous glucose monitoring length of intervention to evaluate long-term glycemic control. Diabetes Technol. Ther. 23, 314–319 (2021).
Cryer, P. E. Glycemic goals in diabetes: trade-off between glycemic control and iatrogenic hypoglycemia. Diabetes 63, 2188–2195 (2014).
Ma, H., Aihara, K. & Chen, L. Detecting causality from nonlinear dynamics with short-term time series. Sci. Rep. 4, 1–10 (2014).
Verma, A. K. et al. Skeletal muscle pump drives control of cardiovascular and postural systems. Sci. Rep. 7, 1–8 (2017).
Nemat, H., Khadem, H., Elliott, J. & Benaissa, M. Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction. Comput. Biol. Med. 153, 106535 (2023).
Breton, M. D. & Kovatchev, B. P. One year real-world use of the control-IQ advanced hybrid closed-loop technology. Diabetes Technol. Ther. 23, 601–608 (2021).
Mujahid, O. Ai-based type 1 diabetes simulator. Github https://doi.org/10.5281/zenodo.10722210 (2024).
Arabic
Dutch
English
French
German
Italian
Portuguese
Russian
Spanish