learning a diagnostic and medical data with deep reinforcement learning
E.Edith Esther,Jaya Shalini J,Savya Sri K,Preetha G
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 22 April,2024 Pages:1879-1885
Abstract
The intersection of machine learning and healthcare has shown promising results, particularly in diagnostic tasks. Deep reinforcement learning (DRL) has emerged as a powerful technique for learning complex decision-making policies from raw data in medical field, refers to medical conditions in patients. However, such features are not always readily available due to the high cost of time and money associating with medical tests. To address this, this study identifies the diagnostic strategy learning problem and proposes a novel framework consisting of three components to learn a diagnostic strategy with limited features. It involves medical history, test results, and other relevant information to reach a diagnosis. The vast amount of information collected from patients, including medical records, lab tests, scans, X- rays or MRIs and genetic data etc. And also utilize secure storage mechanisms such as encrypted databases and secure file systems to store medical data.
Kewords
Machine Learning, Deep Reinforcement Learning
Reference
[1] Rui Zhang, RuiXue, and Ling Liu, “Security and Privacy for Healthcare Blockchains,” 1939-1374 July 01,2021. [2] Alaa Haddad, Mohamed HadiHabaebi, Md. Rafiqul Islam, NurulFadzlinHasbullah, And Suriza Ahmad Zabidi, “Systematic Review on AI-Blockchain Based E-Healthcare Records Management Systems,” Volume 10, 2022 [3] VishwaAmitkumar Patel, Pronaya Bhattacharya, SudeepTanwar, Rajesh Gupta , Gulshan Sharma, Pitshou N. Bokoro , Ravi Sharma, “Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions,” Volume 10, 2022. [4] Abdullah Al Mamun, Sami Azam, Clementine Gritti. “Blockchain-Based Electronic Health Records Management: A Comprehensive Review and Future Research Direction,”Volume 10, 2022. [5] HuaShen, “Enhancing Diagnosis Prediction in Healthcare with Knowledge Based Recurrent Neural Networks,” Volume 11, 2023. [6] H. Malik, N. Fatema, and J. A. Alzubi, AI and Machine Learning Paradigms for Health Monitoring System: Intelligent Data Analytics. Berlin, Germany: Springer, 2021. [7] J. G. D. Ochoa and F. E. Mustafa, ‘‘Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients’ diagnoses,’’ Artif. Intell.Med., vol. 131, Sep. 2022, Art.no. 102359. [8] A. AwadAbdellatif, L. Samara, A. Mohamed, A. Erbad, C. F. Chiasserini, M. Guizani, M. D. O’Connor, and J. Laughton, ‘‘MEdge-chain: Leveraging edge computing and blockchain for efficient medical data exchange,’’ IEEE Internet Things J., vol. 8, no. 21, pp. 15762–15775, Nov. 2021. [9] P. Bhattacharya, S. Tanwar, U. Bodkhe, S. Tyagi, and N. Kumar, ‘‘BinDaaS: Blockchain-based deep-learning as-a-service in health- care 4.0 applications,’’ IEEE Trans. Netw. Sci. Eng., vol. 8, no. 2, pp. 1242–1255, Apr. 2021. [10] C. Hou, K. K. Thekumparampil, G. Fanti, and S. Oh, ‘‘Reducing the communication cost of federated learning through multistage optimization,’’ in Proc. Int. Conf. Learn. Represent., 2022, pp. 1–49. [11] J. Zhu and J. Ma, “A new authentication scheme with anonymity for wireless environments,” IEEE Transactions on Consumer Elec-tronics, vol. 50, no. 1, pp. 231–235, Feb 2004. [12] P. Gope and T. Hwang, “Lightweight and energy-efficient mutual authentication and key agreement scheme with user anonymity for secure communication in global mobility networks,” IEEE Systems Journal, vol. 10, no. 4, pp. 1370– 1379, Dec 2016. [13] J. Ni, K. Zhang, X. Lin, H. Yang, and X. S. Shen, “Ama: Anonymous mutual authentication with traceability in carpooling systems,” in ICC 2016, May 2016, pp. 1–6. [14] G. Yang, Q. Huang, D. S. Wong, and X. Deng, “Universal authen- tication protocols for anonymous wireless communications,” IEEE Transactions on Wireless Communications, vol. 9, no. 1, pp. 168–174, January 2010. [15] Q. Yang, K. Xue, J. Xu, J. Wang, F. Li, and N. Yu, “Anfra: Anony- mous and fast roaming authentication for space information network,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 2, pp. 486–497, Feb 2019.
