Securing Healthcare Systems: Addressing Challenges in Protecting Big Data, AI and ERP Systems

Sunil Kumar Sehrawat

Vol. 10, Jan-Dec 2024

Page Number: 1 - 16

Abstract:

The paper briefly introduces big data and its role in healthcare applications. It highlights how big data architecture and techniques are continuously aiding in managing the rapid growth of data in the healthcare industry. Initially, an empirical study was conducted to analyze the impact of big data in the healthcare sector, revealing that significant advancements have been made. However, envisioning the influence of machine learning and big data on healthcare, a complex and evolving field, remains a challenge that engages the audience's curiosity and interest.

References

• S. Latif, J. Qadir, S. Farooq, and M. Imran, “How 5G wireless (and concomitant technologies) will revolutionize healthcare?” Future Internet, vol. 9, no. 4, p. 93, 2017.
• Z.Yan et al., “Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition,” IEEE Trans. Med. Imag., vol. 35, no. 5, pp. 1332–1343, May 2016
• M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung pattern classification for interstitial lung diseases using a deep convolutional neural network,” IEEE Trans. Med. Imag., vol. 35, no. 5, pp. 1207–1216, May 2016.
• W. Shen, M. Zhou, F. Yang, C. Yang, and J. Tian, “Multi-scale convolutional neural networks for lung nodule classification,” in Proc. Int. Conf. Inf. Process. Med. Imag., 2015, pp. 588–599.
• J. Schlemper, J. Caballero, J. V. Hajnal, A. Price, and D. Rueckert, “A deep cascade of convolutional neural networks forMR image reconstruction,” in Proc. Int. Conf. Inf. Process. Med. Imag., 2017, pp. 647–658.
• J. Mehta and A. Majumdar, “Rodeo: Robust de-aliasing autoencoder for real-time medical image reconstruction,” Pattern Recognit., vol. 63, pp. 499–510, 2017.
• M. Havaei et al., “Brain tumor segmentation with deep neural networks,” Med. Image Anal., vol. 35, pp. 18–31, 2017.
• K. Bourzac, “The computer will see you now,” Nature, vol. 502, no. 3, pp. S92–S94, 2013.
• L. Xing, E. A. Krupinski, and J. Cai, “Artificial intelligence will soon change the landscape of medical physics research and practice,” Med. Phys., vol. 45, no. 5, pp. 1791–1793, 2018.
• B. E. Bejnordi et al., “Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer,” J. Amer. Med. Assoc., vol. 318, no. 22, pp. 2199–2210, 2017.
• P. Rajpurkar et al., “Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning,” 2017, arXiv:1711.05225.
• V. Gulshan et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” J. Amer. Med. Assoc., vol. 316, no. 22, pp. 2402–2410, 2016.
• A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017.
• S. Latif,M.Asim, M. Usman, J. Qadir, and R. Rana, “Automatingmotion correction in multishot MRI using generative adversarial networks,” in Proc. 32nd Conf. Neural Inf. Process. Syst., 2018.
• X.-W. Chen and X. Lin, “Big data deep learning: Challenges and perspectives,” IEEE Access, vol. 2, pp. 514–525, 2014.
• R. Miotto, F.Wang, S.Wang, X. Jiang, and J. T. Dudley, “Deep learning for healthcare: Review, opportunities and challenges,” Briefings Bioinformat., vol. 19, no. 6, pp. 1236–1246, 2017.
• K. Papangelou, K. Sechidis, J. Weatherall, and G. Brown, “Toward an understanding of adversarial examples in clinical trials,” in Proc. Joint Eur. Conf. Mach. Learn. Knowl. Discovery Databases, 2018, pp. 35–51.
• H. Kim, D. C. Jung, and B. W. Choi, “Exploiting the vulnerability of deep learning-based artificial intelligence models in medical imaging: Adversarial attacks,” J. Korean Soc. Radiol., vol. 80, no. 2, pp. 259–273, 2019.
• X. Yuan, P. He, Q. Zhu, and X. Li, “Adversarial examples: Attacks and defenses for deep learning,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 9, pp. 2805–2824, Sep. 2019.
• C. Szegedy et al., “Intriguing properties of neural networks,” 2013, arXiv:1312.6199.
• A. Shafahi et al., “Poison frogs! Targeted clean-label poisoning attacks on neural networks,” in Proc. Adv. Neural Inf. Process. Syst., 2018, pp. 6103–6113.
• S. G. Finlayson, J. D. Bowers, J. Ito, J. L. Zittrain, A. L. Beam, and I. S. Kohane, “Adversarial attacks on medical machine learning,” Science, vol. 363, no. 6433, pp. 1287–1289, 2019.
• V. Chandola, A. Banerjee, andV.Kumar, “Anomaly detection:Asurvey,” ACM Comput. Surv., vol. 41, no. 3, pp. 1–58, 2009.
• A. K. Pandey, P. Pandey, K. Jaiswal, and A. K. Sen, “Datamining clustering techniques in the prediction of heart disease using attribute selection method,” Heart Disease, vol. 14, pp. 16–17, 2013.
• K. Polat and S. Güne¸s, “Prediction of hepatitis disease based on principal component analysis and artificial immune recognition system,” Appl. Math. Comput., vol. 189, no. 2, pp. 1282–1291, 2007.
• M. Alloghani, D. Al-Jumeily, J. Mustafina, A. Hussain, and A. J. Aljaaf, “A systematic review on supervised and unsupervised machine learning algorithms for data science,” in Supervised and Unsupervised Learning for Data Science. Berlin, Germany: Springer, 2020, pp. 3–21.
• M. N. Sohail, J. Ren, andM. U.Muhammad, “A Euclidean group assessment on semi-supervised clustering for healthcare clinical implications based on real-life data,” Int. J. Environ. Res. Public Health, vol. 16, no. 9, p. 1581, 2019.
• A. Zahin, R. Q. Hu et al., “Sensor-based human activity recognition for smart healthcare: A semi-supervised machine learning,” in Proc. Int. Conf. Artif. Intell. Commun. Netw., 2019, pp. 450–472.
• D. Mahapatra, “Semi-supervised learning and graph cuts for consensus based medical image segmentation,” Pattern Recognit., vol. 63, pp. 700–709, 2017.
• W. Bai et al., “Semi-supervised learning for network-based cardiac MR image segmentation,” in Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, 2017, pp. 253–260.

Back Download