Abstract
The medical burden of AIDS is a significant public health problem. However, it is affected by the multiple factors, among which there is yet some vague cognition, and further exploration is necessary. Thus, the artificial neural network (ANN) and restricted Boltzmann machine (RBM) be treated as the infrastructure of deep neural networks (DNN), mainly based on the features of demography, pathology and clinical manifestation of AIDS patient’s medical records to mine the impact factors of AIDS cost. And the proposed model could bring to light the previously uncharted latent knowledge and concepts. Based on reliable healthcare delivery, to inhibit the number of hospital days, intensive care and hospitalized frequency plus other sensitive factors, and avoid secondary infection and exposure to allergic reactions can obviously reduce the AIDS cost.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, Y.Q., Qin, X., Zhou, L., et al.: The AIDS epidemic and economic input impact factors in Chongqing, China, from 2006 to 2012: a spatial-temporal analysis. BMJ Open 5(3) (2015)
Zhang, X.L., Zhang, Y.R., Aleong, T.H., et al.: Factors associated with the Household Income of Persons Living with HIV/AIDS in China. Global J. Health Sci. 4(3), 108–116 (2012)
Harmon, T.M., Fisher, K.A., Mcglynn, M.G., et al.: Exploring the potential health impact and cost-effectiveness of AIDS vaccine within a comprehensive HIV/AIDS response in low and middle-income countries. PLoS ONE 11(1), e0146387 (2015)
Stover, J., Bollinger, L., Izazola, J.A., et al.: What Is required to end the AIDS epidemic as a public health threat by 2030? The Cost and Impact of the Fast-Track Approach. PLoS ONE 11(5), e0154893 (2016)
LeCun, Y., Bengio, Y., Hinton, G.: Deep Learing. Nature Mag. 521(7553), 436–444 (2015)
Ryota, S., Shusuke, Y., Yasutaka, M., et al.: Deep learning application trial to lung cancer diagnosis for medical sensor systems. In: 2016 International SoC Design Conference (ISOCC), pp. 191–192 (2016)
Le, R.N., Bengio, Y.: Representational power of restricted boltzmann machines and deep belief networks. Neural Comput. 20(6), 1631–1649 (2008)
Hinton, G., Osindero, I.Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Tieleman, T., Hinton, G.E.: Using fast weights to improve persistent contrastive divergence. In: Proceedings of the 26th International Conference on Machine Learning, Helsinki, Finland, pp. 1064–1071 (2008)
Deng, L.: A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inf. Proces. 3, 14–43 (2014)
Furundzic, D., Djordjevic, M., Bekic, A.J.: Neural networks approach to early breast cancer detection. Syst Architect 44, 617–633 (1998)
Tieleman, T.: Training restricted Boltzmann machines using approximations to the likelihood gradient. In: Proceedings of 25th International Conference on Machine Learning, New York, pp, 1064–1071. ACM (2008)
Acknowledgement
This work is partially funded through NNSFC Grants #91546112. National Key R&D Program #2016YFC1200702 and #2016QY02D0200.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, D., Cao, Z., Li, S. (2017). Using Deep Learning to Mine the Key Factors of the Cost of AIDS Treatment. In: Chen, H., Zeng, D., Karahanna, E., Bardhan, I. (eds) Smart Health. ICSH 2017. Lecture Notes in Computer Science(), vol 10347. Springer, Cham. https://doi.org/10.1007/978-3-319-67964-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-67964-8_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67963-1
Online ISBN: 978-3-319-67964-8
eBook Packages: Computer ScienceComputer Science (R0)