Blockchain and Machine Learning in EHR Security: A Systematic Review

被引:2
|
作者
Zukaib, Umer [1 ]
Cui, Xiaohui [1 ]
Hassan, Mir [2 ]
Harris, Sheetal [1 ]
Hadi, Hassan Jalil [1 ]
Zheng, Chengliang [1 ]
机构
[1] Wuhan Univ, Sch Cyber Sci & Engn, Key Lab Aerosp Informat Secur & Trusted Comp, Minist Educ, Wuhan 430072, Peoples R China
[2] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Trento, Italy
关键词
Medical services; Security; Blockchains; Systematics; Privacy; Machine learning; Data models; Smart healthcare; Federated learning; Deep learning; Electronic medical records; Blockchain technology; smart healthcare; federated learning; securing patient records; deep learning; electronic health records; CYBER-PHYSICAL SYSTEMS; HEALTH RECORD; CHALLENGES; PRIVACY; PREDICTION; SCHEME; MODEL; SCALABILITY; TECHNOLOGY;
D O I
10.1109/ACCESS.2023.3333229
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Background: The rapid development of modern technologies renders a convenient and efficient solution to implement Electronic Health Records (EHRs) systems. The rapid growth of healthcare data has a distinctive attribute of digital transformations. The big datasets of healthcare, their complexity and their dynamic nature have posed severe challenges associated with the analysis, pre-processing, privacy, security, storage, usability and data exchange. Material and Methods: We have performed the Systematic Literature Review (SLR) and followed the Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. SLR refers to the methodology that discovers, analyses and accesses recent research literature related to the subject field. The research papers were searched from academic repositories like IEEE, WOS, Scopus and PubMed for the previous five years on March 2023. Results: The designed search string provides 199 research articles in total. We filter the research articles based on inclusion-exclusion strategies and quality assessment metrics. Six main criteria for research inclusion-exclusion for SLR are formulated. These works of literature insight into 1) the issues associated with interoperability and security of EHRs by using the Blockchain (BC) technology, 2) different frameworks and tools to improve privacy and security in the healthcare domain, 3) the open issues of using BC technology in the electronic healthcare domain, 4) the standardized ways to store EHRs, 5) various ways to handle the big data using the BC systems and 6) the usage of Federated Learning (FL) to preserve the privacy of EHRs in the healthcare domain. We acquired 46 research articles based on the criteria (inclusion-exclusion) that investigate the above-mentioned issues. Conclusion: The SLR will serve as the state-of-the-art (SOTA) for future researchers in the field of BC in healthcare. Additionally, the paper provides insights to the new researchers to revolutionize the healthcare domain by adopting the latest digitalized technologies. The proposed study identified various reflections. It analyzed the architectural mechanism that supports the security and interoperability of EHRs. Secondly, the study described different tools and frameworks to improve the privacy and security of EHRs using the BC. Thirdly, the open issues of storing and preserving the EHRs using BC in the healthcare system were determined. Fourth, it analyzed and provided a detailed view of using standardized ways for storing and handling big data by using the BC system. Lastly, the usage of FL to preserve the privacy of EHRs was analyzed.
引用
收藏
页码:130230 / 130256
页数:27
相关论文
共 50 条
  • [1] Blockchain and Machine Learning: A Critical Review on Security
    Taherdoost, Hamed
    INFORMATION, 2023, 14 (05)
  • [2] Machine Learning for Cloud Security: A Systematic Review
    Nassif, Ali Bou
    Abu Talib, Manar
    Nasir, Qassim
    Albadani, Halah
    Dakalbab, Fatima Mohamad
    IEEE ACCESS, 2021, 9 : 20717 - 20735
  • [3] Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security
    Qayyum, Adnan
    Ijaz, Aneeqa
    Usama, Muhammad
    Iqbal, Waleed
    Qadir, Junaid
    Elkhatib, Yehia
    Al-Fuqaha, Ala
    FRONTIERS IN BIG DATA, 2020, 3
  • [4] Systematic Literature Review of Machine Learning for IoT Security
    Yemmanuru, Prathibha Kiran
    Yeboah, Jones
    Esther, Khakata N. G.
    2023 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE, CSCI 2023, 2023, : 227 - 233
  • [5] A systematic review of innovations for real-time image security in IoT applications using machine learning and blockchain
    Rai, Manish
    Kumar, Sunil
    Rathore, Pramod Singh
    JOURNAL OF INTELLIGENT MANUFACTURING, 2024,
  • [6] Machine learning approaches to IoT security: A systematic literature review
    Ahmad, Rasheed
    Alsmadi, Izzat
    INTERNET OF THINGS, 2021, 14
  • [7] A systematic literature review of blockchain cyber security
    Taylor, Paul J.
    Dargahi, Tooska
    Dehghantanha, Ali
    Parizi, Reza M.
    Choo, Kim-Kwang Raymond
    DIGITAL COMMUNICATIONS AND NETWORKS, 2020, 6 (02) : 147 - 156
  • [8] A systematic literature review of blockchain cyber security
    Paul JTaylor
    Tooska Dargahi
    Ali Dehghantanha
    Reza MParizi
    KimKwang Raymond Choo
    Digital Communications and Networks, 2020, 6 (02) : 147 - 156
  • [9] Convergence of blockchain, IoT, and machine learning: exploring opportunities and challenges - a systematic review
    Aounzou, Youssef
    Boulaalam, Abdelhak
    Kalloubi, Fahd
    INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, 2025, 18 (01):
  • [10] Machine Learning and Blockchain: A Bibliometric Study on Security and Privacy
    Valencia-Arias, Alejandro
    Gonzalez-Ruiz, Juan David
    Flores, Lilian Verde
    Vega-Mori, Luis
    Rodriguez-Correa, Paula
    Santos, Gustavo Sanchez
    INFORMATION, 2024, 15 (01)