Abstract
The main motive to present this chapter is to provide effective deep learning algorithms for personalized healthcare services and to make the reader understand the importance of deep learning in the field of healthcare. We realize that acquiring ability and significant viewpoints from nuanced, high-dimensional, and heterogeneous biomedical information keeps on being a significant test in medical care change. Electronic wellbeing reports, imaging, omics, sensor information, and text are instances of nuanced, heterogeneous, gravely explained, and generally unstructured information that have arisen in contemporary biomedical science. As feature engineering is needed in traditional data mining approach to extract efficient and more scalable features from data, and after that predicting and clustering the models make it more challenging in these steps, especially in case of complicated data, the latest advancements in deep learning in the field of healthcare give effective calculations to get start to finish models from complex information. In the coming days, it is believed that deep learning will have a major role in converting big and complex biomedical data into improved human health. However, we also notice limitations and the need for improved method creation and implementations, especially in terms of domain experts’ and citizen scientists’ comprehension. To close the gap between deep learning models and human interpretability, we propose designing holistic and practical interpretable algorithms.
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
Similar content being viewed by others
References
Muller, H., & Unay, D. (2017). Retrieval from and understanding of large-scale multi-modal medical datasets: A review. IEEE Transactions on Multimedia, 19(9), 2093–2104.
Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017). Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21.
Alam, M., Vidyaratne, L. S., & Iftekharuddin, K. M. (2018). Sparse simultaneous recurrent deep learning for robust facial expression recognition. IEEE Transactions on Neural Networks and Learning Systems, 29(10), 4905–4916.
Mishra, S., Panda, A., & Tripathy, K. H. (2018). Implementation of re-sampling technique to handle skewed data in tumor prediction. Journal of Advanced Research in Dynamical and Control Systems, 10, 526–530.
Tobore, I., Li, J., Yuhang, L., Al-Handarish, Y., Kandwal, A., Nie, Z., & Wang, L. (2019). Deep learning intervention for health care challenges: Some biomedical domain considerations. JMIR mHealth and uHealth, 7(8), e11966.
Sahoo, S., Das, M., Mishra, S., & Suman, S. (2021). A hybrid DTNB model for heart disorders prediction. In Advances in electronics, communication and computing (pp. 155–163). Springer.
Yousoff, S. N. M., Baharin, A., & Abdullah, A. (2016). A review on optimization algorithm for deep learning method in bioinformatics field. In 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).
Jena, L., Mishra, S., Nayak, S., Ranjan, P., & Mishra, M. K. (2021). Variable optimization in cervical cancer data using particle swarm optimization. In Advances in electronics, communication and computing (pp. 147–153). Springer.
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning—ICML’08.
Roy, S. N., Mishra, S., & Yusof, S. M. (2021). Emergence of drug discovery in machine learning. In Technical advancements of machine learning in healthcare (Vol. 936, p. 119).
Masci, J., Meier, U., Cireşan, D., & Schmidhuber, J. (2011). Stacked convolutional auto-encoders for hierarchical feature extraction. In Lecture notes in computer science (Vol. 52, pp. 52–59). Springer Berlin Heidelberg.
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Van Esesn, B. C., Awwal, A. A. S., & Asari, V. K. (2018). The history began from AlexNet: A comprehensive survey on deep learning approaches. arXiv [cs.CV].
Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166.
Matsugu, M., Mori, K., Mitari, Y., & Kaneda, Y. (2003). Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Networks, 16(5–6), 555–559.
Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology, 160(1), 106–154.
Tripathy, H. K., Mishra, S., Thakkar, H. K., & Rai, D. (2021). CARE: A collision-aware mobile robot navigation in grid environment using improved breadth first search. Computers & Electrical Engineering, 94, 107327.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.
Salakhutdinov, R., & Brain, H. L. (2010). Efficient learning of deep Boltzmann machines. Mlr.press. [Online]. Available: http://proceedings.mlr.press/v9/salakhutdinov10a/salakhutdinov10a.pdf. Accessed June 28, 2021.
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.
Kim, J. Y., Lee, H. E., Choi, Y. H., Lee, S. J., & Jeon, J. S. (2019). CNN-based diagnosis models for canine ulcerative keratitis. Science and Reports, 9(1), 14209.
Ryu, S., Noh, J., & Kim, H. (2016). Deep neural network based demand side short term load forecasting. Energies, 10(1), 3.
Tripathy, H. K., Mallick, P. K., & Mishra, S. (2021). Application and evaluation of classification model to detect autistic spectrum disorders in children. International Journal of Computer Applications in Technology, 65(4), 368–377.
Mishra, S., Mohapatra, S. K., Mishra, B. K., & Sahoo, S. (2018). Analysis of mobile cloud computing: Architecture, applications, challenges, and future perspectives. In Applications of security, mobile, analytic, and cloud (SMAC) technologies for effective information processing and management (pp. 81–104). IGI Global.
Xu, M., Papageorgiou, D. P., Abidi, S. Z., Dao, M., Zhao, H., & Karniadakis, G. E. (2017). A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLoS Computational Biology, 13(10), e1005746.
Nguyen, P., Tran, T., Wickramasinghe, N., & Venkatesh, S. (2017). $\mathtt Deepr$: A convolutional net for medical records. IEEE Journal of Biomedical and Health Informatics, 21(1), 22–30.
Lin, C., Hsu, C. J., Lou, Y. S., Yeh, S. J., Lee, C. C., Su, S. L., & Chen, H. C. (2017). Artificial intelligence learning semantics via external resources for classifying diagnosis codes in discharge notes. Journal of Medical Internet Research, 19(11), e380.
Wang, S.-H., Lv, Y.-D., Sui, Y., Liu, S., Wang, S.-J., & Zhang, Y.-D. (2017). Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. Journal of Medical Systems, 42(1), 2.
Zhang, J., & Wu, Y. (2017). A new method for automatic sleep stage classification. IEEE Transactions on Biomedical Circuits and Systems, 11(5), 1097–1110.
Dominguez Veiga, J. J., O’Reilly, M., Whelan, D., Caulfield, B., & Ward, T. E. (2017). Feature-free activity classification of inertial sensor data with machine vision techniques: Method, development, and evaluation. JMIR mHealth and uHealth, 5(8), e115.
Sacha, D., Sedlmair, M., Zhang, L., Lee, J. A., Weiskopf, D., North, S., & Keim, D. (2016). Human-centered machine learning through interactive visualization. In ESANN 2016: 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings (pp. 641–646), Bruges, April 27–29, 2016.
Yang, X., Zhang, T., & Xu, C. (2015). Cross-domain feature learning in multimedia. IEEE Transactions on Multimedia, 17(1), 64–78.
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1).
Chattopadhyay, A., Mishra, S., & González-Briones, A. (2021). Integration of machine learning and IoT in healthcare domain. In Hybrid artificial intelligence and IoT in healthcare (pp. 223–244). Springer.
Lindsey, M. L., Kassiri, Z., Virag, J. A. I., de Castro Brás, L. E., & Scherrer-Crosbie, M. (2018). Guidelines for measuring cardiac physiology in mice. American Journal of Physiology-Heart and Circulatory Physiology, 314(4), H733–H752.
Raina, R., Madhavan, A., & Ng, A. Y. (2009). Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th Annual International Conference on Machine Learning—ICML’09.
Page, A., Turner, J. T., Mohsenin, T., & Oates, T. (2014). Comparing raw data and feature extraction for seizure detection with deep learning methods. Umbc.edu. [Online]. Available: http://eehpc.csee.umbc.edu/publications/pdf/2014/Adam_FLAIRS14.pdf. Accessed June 28, 2021.
Ahmed, E., Yaqoob, I., Gani, A., Imran, M., & Guizani, M. (2016). Internet-of-things-based smart environments: State of the art, taxonomy, and open research challenges. IEEE Wireless Communications, 23(5), 10–16.
Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., Yunsheng, M., Chen, S., & Hou, P. (2018). A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure. IEEE Transactions on Services Computing, 11(2), 249–261.
Mukherjee, D., Tripathy, H. K., & Mishra, S. (2021). Scope of medical bots in clinical domain. In Technical advancements of machine learning in healthcare (Vol. 936, p. 339).
Brady, C. J., Mudie, L. I., Wang, X., Guallar, E., & Friedman, D. S. (2017). Improving consensus scoring of crowdsourced data using the Rasch model: Development and refinement of a diagnostic instrument. Journal of Medical Internet Research, 19(6), e222.
Bayen, E., Jacquemot, J., Netscher, G., Agrawal, P., Tabb Noyce, L., & Bayen, A. (2017). Reduction in fall rate in dementia managed care through video incident review: Pilot study. Journal of Medical Internet Research, 19(10), e339.
Doerr, M., Maguire Truong, A., Bot, B. M., Wilbanks, J., Suver, C., & Mangravite, L. M. (2017). Formative evaluation of participant experience with mobile eConsent in the app-mediated Parkinson mPower study: A mixed methods study. JMIR mHealth and uHealth, 5(2), e14.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Mishra, A., Mohapatra, S.S., Bisoy, S.K. (2022). Effective Deep Learning Algorithms for Personalized Healthcare Services. In: Mishra, S., Tripathy, H.K., Mallick, P., Shaalan, K. (eds) Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. Studies in Computational Intelligence, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-19-1076-0_8
Download citation
DOI: https://doi.org/10.1007/978-981-19-1076-0_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1075-3
Online ISBN: 978-981-19-1076-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)