cirpublications.com-R4OU-1790880682-u926316562 1790880682 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2023 2 1 Hiroshi Nakamura Yuta Kato 2023 10.68159/u926316562 https://cirpublications.com/pub/journal/1/article/u926316562 Manzini E, Vlacho B, Franch-Nadal J, Escudero J, Génova A, Reixach E, et al. Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records. J Biomed Inform. 2022;135:104218. 10.1016/j.jbi.2022.104218 Lavikainen P, Aarnio E, Linna M, Jalkanen K. Data-driven long-term glycaemic control trajectories and their associated health and economic outcomes in Finnish patients with incident type 2 diabetes. PLoS One. 2022;17(6):e0269245. 10.1371/journal.pone.0269245 Baowaly MK, Lin CC, Liu CL, Chen KT. Synthesizing electronic health records using improved generative adversarial networks. J Am Med Inform Assoc. 2019;26(3):228-41. Rashidian S, Wang F, Moffitt R, Garcia V, Dutt A, Chang W, et al. SMOOTH-GAN: towards sharp and smooth synthetic EHR data generation. In: Artif Intell Med. 2020. p. 37-48. 10.1007/978-3-030-59137-3_4 Yoon J, Jarrett D, Van der Schaar M. Time-series generative adversarial networks. Adv Neural Inf Process Syst. 2019;32:5508-18. Ghosheh G, Li J, Zhu T. A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources. arXiv [Preprint]. 2022:arXiv:2203.07018. 10.48550/arXiv.2203.07018 Zhang Z, Yan C, Lasko TA, Sun J, Malin BA. SynTEG: a framework for temporal structured electronic health data simulation. J Am Med Inform Assoc. 2021;28(3):596-604. Dworzynski P, Aasbrenn M, Rostgaard K, Melbye M, Gerds TA, Hjalgrim H, et al. Nationwide prediction of type 2 diabetes comorbidities. Sci Rep. 2020;10(1):1776. 10.1038/s41598-020-58673-y Hernandez M, Epelde G, Alberdi A, Cilla R, Rankin D. Synthetic data generation for tabular health records: a systematic review. Neurocomputing. 2022;493:28-45. 10.1016/j.neucom.2022.04.043 Yale A, Dash S, Dutta R, Guyon I, Pavao A, Bennett KP. Generation and evaluation of privacy preserving synthetic health data. Neurocomputing. 2020;416:244-55. 10.1016/j.neucom.2020.07.070 Esteban C, Hyland SL, Rätsch G. Real-valued (medical) time series generation with recurrent conditional GANs. arXiv [Preprint]. 2017:arXiv:1706.02633. 10.48550/arXiv.1706.02633 Zhang Z, Yan C, Malin BA. Keeping synthetic patients on track: feedback mechanisms to mitigate performance drift in longitudinal health data simulation. J Am Med Inform Assoc. 2022;29(11):1890-8. Kirk IK, Simon C, Banasik K, Holm PC, Haue AD, Jensen PB, et al. Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining. Elife. 2019;8:e44941. 10.7554/eLife.44941 Dash S, Yale A, Guyon I, Bennett KP. Medical time-series data generation using generative adversarial networks. In: Artif Intell Med. 2020. p. 382-91. 10.1007/978-3-030-59137-3_35 Gupta M, Phan TL, Bunnell HT, Beheshti R. Concurrent imputation and prediction on EHR data using bi-directional GANs: Bi-GANs for EHR imputation and prediction. In: Proc 12th ACM Int Conf Bioinformatics Comput Biol Health Inform. 2021. p. 1-9. 10.1145/3459930.3469521 Sun S, Wang F, Rashidian S, Kurc T, Abell-Hart K, Hajagos J, et al. Generating longitudinal synthetic EHR data with recurrent autoencoders and generative adversarial networks. In: Data Management and Analytics for Medicine and Healthcare. 2021. p. 153-65. 10.1007/978-3-030-93653-2_14 Venugopal R, Shafqat N, Venugopal I, Tillbury BM, Stafford HD, Bourazeri A. Privacy preserving generative adversarial networks to model electronic health records. Neural Netw. 2022;153:339-48. 10.1016/j.neunet.2022.06.020 Torfi A, Fox EA, Reddy CK. Differentially private synthetic medical data generation using convolutional GANs. Inf Sci. 2022;586:485-500. 10.1016/j.ins.2021.11.055 Yale A, Dash S, Dutta R, Guyon I, Pavao A, Bennett KP. Assessing privacy and quality of synthetic health data. In: Proc Conf Artif Intell Data Discov Reuse. 2019. p. 1-4. 10.1145/3313294.3313388 Xiang X, Duan S, Pan H, Han P, Cao J, Liu C. From one-hot encoding to privacy-preserving synthetic electronic health records embedding. In: Proc Int Conf Cyberspace Innov Adv Technol. 2020. p. 407-13. 10.1145/3444370.3444586 Ehrhart M, Resch B, Havas C, Niederseer D. A conditional GAN for generating time series data for stress detection in wearable physiological sensor data. Sensors (Basel). 2022;22(16):5969. 10.3390/s22165969 Brophy E, De Vos M, Boylan G, Ward T. Multivariate generative adversarial networks and their loss functions for synthesis of multichannel ECGs. IEEE Access. 2021;9:158936-45. 10.1109/ACCESS.2021.3129471 Lou J, Wang Y, Li L, Zeng D. Learning latent heterogeneity for type 2 diabetes patients using longitudinal health markers in electronic health records. Stat Med. 2021;40(8):1930-46. 10.1002/sim.8887 Gopalan A, Mishra P, Alexeeff SE, Blatchins MA, Kim E, Man AH, et al. Prevalence and predictors of delayed clinical diagnosis of type 2 diabetes: a longitudinal cohort study. Diabet Med. 2018;35(12):1655-62. 10.1111/dme.13712 Urina-Jassir M, Herrera-Parra LJ, Hernandez Vargas JA, Valbuena-García AM, Acuña-Merchán L, Urina-Triana M. The effect of comorbidities on glycemic control among Colombian adults with diabetes mellitus: a longitudinal approach with real-world data. BMC Endocr Disord. 2021;21(1):128. 10.1186/s12902-021-00789-y Bing S, Dittadi A, Bauer S, Schwab P. Conditional generation of medical time series for extrapolation to underrepresented populations. PLOS Digit Health. 2022;1(7):e0000074. 10.1371/journal.pdig.0000074 Brophy E, Wang Z, She Q, Ward T. Generative adversarial networks in time series: A survey and taxonomy. arXiv [Preprint]. 2021:arXiv:2107.11098. 10.48550/arXiv.2107.11098 Liu D, Wu Y, Hong D, Wang S. Time series data augmentation method of small sample based on optimized generative adversarial network. Concurr Comput Pract Exp. 2022;34(27):e7331. 10.1002/cpe.7331 Larrea X, Hernandez M, Epelde G, Beristain A, Molina C, Alberdi A, et al. Synthetic subject generation with coupled coherent time series data. Eng Proc. 2022;18(1):7. 10.3390/engproc2022018007