Abstract
Analyzing massive patient-centric Electronic Health Records (EHRs) becomes a key to success for improving health care and treatment. However, the amount of these data is limited and the access to EHRs is difficult due to the issue of patient privacy. Thus high quality synthetic EHRs data is necessary to alleviate these issues. In this paper, we propose a Sequentially Coupled Generative Adversarial Network (SC-GAN) to generate continuous patient-centric data, including patient state and medication dosage data. SC-GAN consists of two generators which coordinate the generation of patient state and medication dosage in a unified model, revealing the clinical fact that the generation of patient state and medication dosage data have noticeable mutual influence on each other. To verify the quality of the synthetic data, we conduct comprehensive experiments to employ these data on real medical tasks, showing that data generated from SC-GAN leads to better performance than the data from other generative models.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Gostin, L.O., et al.: Beyond the HIPAA Privacy Rule: Enhancing Privacy. Improving Health Through Research. National Academies Press, Washington, DC (2009)
McLachlan, S., Dube, K., Gallagher, T.: Using the caremap with health incidents statistics for generating the realistic synthetic electronic healthcare record. In: Healthcare Informatics (ICHI), pp. 439–448 (2016)
Buczak, A.L., Babin, S., Moniz, L.: Data-driven approach for creating synthetic electronic medical records. BMC Med. Inform. Decis. Making 10, 59 (2010)
Beaulieu-Jones, B.K., et al.: Privacy-preserving generative deep neural networks support clinical data sharing, p. 159756. BioRxiv, C.S. (2017)
Yahi, A., Vanguri, R., Elhadad, N., Tatonetti, N.P.: Generative adversarial networks for electronic health records: a framework for exploring and evaluating methods for predicting drug-induced laboratory test trajectories. In: NIPS (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)
Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR, pp. 5967–5976 (2017)
Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: DRAW: a recurrent neural network for image generation. In: ICML, pp. 1462–1471 (2015)
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: ICML, pp. 2642–2651 (2017)
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: AAAI, pp. 2852–2858 (2017)
William, F., Goodfellow, I., Dai, A.M.: MaskGAN: better text generation via filling in the \(\_\). In: ICLR (2018)
Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W.F., Sun, J.: Generating multi-label discrete electronic health records using generative adversarial networks. Machine Learning for Healthcare (2017)
Raghu, A., Komorowski, M., Celi, L.A., Szolovits, P., Ghassemi, M.: Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach. In: Proceedings of the Machine Learning for Health Care, pp. 147–163 (2017)
Wang, L., Zhang, W., He, X., Zha, H.: Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation. In: KDD, pp. 2447–2456. ACM (2018)
Waechter, J., et al.: Interaction between fluids and vasoactive agents on mortality in septic shock: a multicenter, observational study. Criti. Care Med. 42(10), 2158–2168 (2014)
Denton, E.L., et al.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: NIPS, pp. 1486–1494 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS, pp. 3104–3112 (2014)
Casella, P., Paiva, A.: MAgentA: an architecture for real time automatic composition of background music. In: de Antonio, A., Aylett, R., Ballin, D. (eds.) IVA 2001. LNCS (LNAI), vol. 2190, pp. 224–232. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44812-8_18
Zhu, H., et al.: Xiaoice band: a melody and arrangement generation framework for pop music. In: KDD, pp. 2837–2846 (2018)
Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: NIPS, pp. 1171–1179 (2015)
Mogren, O.: C-RNN-GAN: continuous recurrent neural networks with adversarial training. CoRR abs/1611.09904 (2016)
Office for Civil Rights: Guidance regarding methods for de-identification of protected health information in accordance with the health insurance portability and accountability act (HIPAA) privacy rule. U.S. Department of Health and Human Services (2013)
Li, C.Y., Liang, X., Hu, Z., Xing, E.P.: Hybrid retrieval-generation reinforced agent for medical image report generation. arXiv preprint arXiv:1805.08298 (2018)
Esteban, C., Hyland, S.L., Rätsch, G.: Real-valued (medical) time series generation with recurrent conditional GANs. arXiv preprint arXiv:1706.02633 (2017)
Hoang, Q., Nguyen, T.D., Le, T., Phung, D.: Multi-generator generative adversarial nets. In: ICLR (2017)
Wang, L., Zhang, W., He, X., Zha, H.: Personalized prescription for comorbidity. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018. LNCS, vol. 10828, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91458-9_1
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: NIPS, pp. 2234–2242 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)
Weng, W.H., Gao, M., He, Z., Yan, S., Szolovits, P.: Representation and reinforcement learning for personalized glycemic control in septic patients. In: NIPS Workshop (2017)
Singer, M., et al.: The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315(8), 801–810 (2016)
Bajor, J.M., ALasko, T.: Predicting medications from diagnostic codes with recurrent neural networks. In: ICLR (2017)
Pearson, K.: Notes on regression and inheritance in the case of two parents. Proc. R. Soc. London 58, 240–242 (1895)
Acknowledgements
This work is partially supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000904, NSFC (61702190, U1609220).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, L., Zhang, W., He, X. (2019). Continuous Patient-Centric Sequence Generation via Sequentially Coupled Adversarial Learning. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-18579-4_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18578-7
Online ISBN: 978-3-030-18579-4
eBook Packages: Computer ScienceComputer Science (R0)