Blockchain-Based Medical Decision Support System

Authors

  • Tetiana Hovorushchenko Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine
  • Yelyzaveta Hnatchuk Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine
  • Vitaliy Osyadlyi Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine
  • Mariia Kapustian Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine
  • Artem Boyarchuk Department of Law, Tallinna Tehnikaülikool (TalTech), Tallinn, Estonia

DOI:

https://doi.org/10.13052/jcsm2245-1439.123.1

Keywords:

Blockchain, Medical Decisions, Medical Data, Medical Information, Security, Reliability, Medical Decision Support System (MDSS)

Abstract

An urgent task at this moment is the use of blockchain technology to ensure the security of medical decision support systems (MDSS). Our research is devoted to development of blockchain-based MDSS (regarding possibility or impossibility of organ and tissue donation/transplantation, regarding possibility or impossibility of using reproductive technologies in the treatment of infertility). The developed blockchain-based medical decision support system provides reliable protection and security of medical information through the use of blockchain technology, provides support of decision regarding possibility or impossibility of organ and tissue donation/transplantation, provides support of decision regarding possibility or impossibility of use of reproductive technologies in the infertility treatment. The proposed blockchain-based medical decision support system: automates medical decision-making processes, minimizes the human factor and its influence on the medical decision process, and takes into account the norms of current legislation when making medical decisions, thereby allowing not to pay for the services of a hired lawyer, and also works with verified and protected medical data entered in the blockchain, which allows you to get rid of leaks of medical information and to ensure reliable protection of medical data.

Downloads

Download data is not yet available.

Author Biographies

Tetiana Hovorushchenko, Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine

Tetiana Hovorushchenko received the bachelor degree in 2001 and master degree in 2002 in Khmelnytskyi National University (Ukraine). She received the Doctor of Engineering Sciences degree in 2018 in Ukrainian Academy of Printing (Ukraine). She is the head of department of computer engineering and information systems at Khmelnytskyi National University (Ukraine). Research interests: medical decision support systems, software quality evaluation and assurance. She has been serving as a reviewer for many highly-respected journals.

Yelyzaveta Hnatchuk, Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine

Yelyzaveta Hnatchuk received the bachelor degree in 2002 and master degree in 2003 in Khmelnytskyi National University (Ukraine). She received the Doctor Philosophy degree in Engineering Sciences in 2008 in Lviv Polytechnic National University (Ukraine). She is the associate professor of department of computer engineering and information systems at Khmelnytskyi National University (Ukraine). Research interests: medical decision support systems.

Vitaliy Osyadlyi, Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine

Vitaliy Osyadlyi received the bachelor degree in 2020 and master degree in 2022 in Khmelnytskyi National University (Ukraine). He is the PhD student of department of computer engineering and information systems at Khmelnytskyi National University (Ukraine). Research interests: medical data management and processing based on blockchain technologies.

Mariia Kapustian, Department of Computer Engineering & Information Systems, Khmelnytskyi National University, Khmelnytskyi, Ukraine

Mariia Kapustian received the bachelor degree in 2004 and master degree in 2005 in National Aviation University (Ukraine). She received the Doctor Philosophy degree in Engineering Sciences in 2009 in State University of Information-Communication Technologies (Ukraine). She is the of department of computer engineering and information systems at Khmelnytskyi National University (Ukraine). Research interests: systems of information protection, secure corporate networks.

Artem Boyarchuk, Department of Law, Tallinna Tehnikaülikool (TalTech), Tallinn, Estonia

Artem Boyarchuk received the bachelor degree in 2004 and master degree in 2005 in National Aerospace University “Kharkiv Polytechnic Institute” (Ukraine). He received the Doctor Philosophy degree in Engineering Sciences in 2012 in National Aerospace University “Kharkiv Polytechnic Institute” (Ukraine). He is the postdoctoral researcher at department of law, TalTech/Tallinn University of Technology (Estonia). Research interests: information technologies for web-services reliability, artificial intelligence for law.

References

S. Swain, B. Bhushan, G. Dhiman, and W. Viriyasitavat. Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey. Archives of Computational Methods in Engineering, 29(6): 3981–4003, 2022.

I. Izonin, H. Kutucu, and K. K. Singh. Smart systems and data-driven services in healthcare. Computers in Biology and Medicine: 106704, 2022.

V. Levashenko, E. Zaitseva, M. Kvassay, T. Deserno. Reliability estimation of healthcare systems using Fuzzy Decision Trees. In: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, pages 331–340, 2016

S. Iyanna, P. Kaur, P. Ractham, S. Talwar, and A. Najmul Islam. Digital transformation of healthcare sector. What is impeding adoption and continued usage of technology-driven innovations by end-users? Journal of Business Research, 153: 150–161, 2022.

P. Silva, V. Dahlke, M. Smith, W. Charles, J. Gomez, M. Ory, and K. Ramos. An Idealized Clinicogenomic Registry to Engage Underrepresented Populations Using Innovative Technology. Journal of Personalized Medicine, 12(5): 713, 2022.

A. Monrat, O. Schelén, and K. Andersson. A survey of blockchain from the perspectives of applications, challenges, and opportunities. IEEE Access, 7: 117134–117151, 2019.

A. Hajian, V. Prybutok, and H.-C. Chang. An empirical study for blockchain-based information sharing systems in electronic health records: A mediation perspective. Computers in Human Behavior, 138: 107471, 2023.

V. Merlo, G. Pio, F. Giusto, and M. Bilancia. On the exploitation of the blockchain technology in the healthcare sector: A systematic review. Expert Systems with Applications, 2131: 118897, 2023.

I. Izonin, R. Tkachenko, N. Shakhovska, and B. Ilchyshyn. A Two-Step Data Normalization Approach for Improving Classification Accuracy in the Medical Diagnosis Domain. Mathematics, 10(11): 1942, 2022.

Z. Dodevski, and V. Trajkovik. Decentralizing the health information exchange systems-A blockchain based approach. In: Proceedings of 15th International Conference on Informatics and Information Technologies, pages 43–49, 2018.

E. Çalık, H. Kaya, and F. Çelebi. A novel method to ensure the security of the shared medical data using smart contracts: Organ transplantation sample. Concurrency and Computation: Practice and Experience, 34(925): e6752, 2022.

Y. Wu, C. Liu, L. Sebald, P. Nguyen, and Y. Yesha. Apply Trust Computing and Privacy Preserving Smart Contracts to Manage, Share, and Analyze Multi-site Clinical Trial Data. Lecture Notes in Networks and Systems, 541: 3–14, 2023.

M. Y. Ali, S. Ahmed, M. I. Hossain, A. B. M. Alim Al Islam, and J. Noor. Electronic Health Record’s Security and Access Control Using Blockchain and IPFS. Lecture Notes in Networks and Systems, 447: 493–505, 2023.

D. Doreen Hephzibah Miriam, D. Dahiya, Nitin, and C. Rene Robin. Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology. Intelligent Automation and Soft Computing, 35(2): 1889–1906, 2023.

S. Qamar. Healthcare data analysis by feature extraction and classification using deep learning with cloud based cyber security. Computers and Electrical Engineering, 104: 108406, 2022.

T. Hovorushchenko, A. Moskalenko, and V. Osyadlyi. Methods of Medical Data Management Based on Blockchain Technologies. Journal of Reliable Intelligent Environments, 2022.

L. M. K. Al-Ebbini. An Efficient Allocation for Lung Transplantation Using Ant Colony Optimization. Intelligent Automation and Soft Computing, 35(2): 1971–1985, 2023.

N. Gotlieb, A. Azhie, D. Sharma, A. Spann, N.-J. Suo, J. Tran, A. Orchanian-Cheff, B. Wang, A. Goldenberg, M. Chassé, H. Cardinal, and J. Cohen. The promise of machine learning applications in solid organ transplantation. Digital Medicine, 5(1): 89, 2022.

N. Taherkhani, M. Sepehri, R. Khasha, and S. Shafaghi. Ranking patients on the kidney transplant waiting list based on fuzzy inference system. BMC Nephrology, 23(1): 31, 2022.

M. Levan, C. Trahan, S. Klitenic, J. Hewlett, T. Strout, M. Levan, K. Vanterpool, D. Segev, B. Adams, A. Massie, and P. Niles. Short Report: Evaluating the Effects of Automated Donor Referral Technology on Deceased Donor Referrals. Transplantation Direct, 8(8): E1330, 2022.

T. Hovorushchenko, A. Herts, Ye. Hnatchuk, and O. Sachenko. Supporting the decision-making about the possibility of donation and transplantation based on civil law grounds. Advances in Intelligent Systems and Computing, 1246: 357–376, 2021.

D. Hawashin, R. Jayaraman, K. Salah, I. Yaqoob, M. Simsekler, and S. Ellahham. Blockchain-Based Management for Organ Donation and Transplantation. IEEE Access, 10: 59013–59025, 2022.

P. Wijayathilaka, G. Pahala, K. De Silva, A. Athukorala, K. Kahandawaarachchi, and K. Pulasinghe. Secured, intelligent blood and organ donation management system – ‘Lifeshare’. In: Proceedings of 2nd International Conference on Advancements in Computing, pages 374–379, 2020.

C. Niyigena, S. Seol, and A. Lenskiy. Survey on Organ Allocation Algorithms and Blockchain-based Systems for Organ Donation and Transplantation. In: Proceedings of International Conference on ICT Convergence, pages 173–178, 2020.

S. Khodabandelu, Z. Basirat, S. Khaleghi, S. Khafri, H. Montazery Kordy, and M. Golsorkhtabaramiri. Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them. BMC Medical Informatics and Decision Making, 22(1): 228, 2022.

D. Bhaskar, T. Chang, and S. Wang. Current trends in artificial intelligence in reproductive endocrinology. Current Opinion in Obstetrics and Gynecology, 34(4): 159–163, 2022.

S. Khder, E. Mohamed, and I. Yassine. A Clustering-Based Fusion System for Blastomere Localization. Biomedical Engineering – Applications, Basis and Communications, 34(41): 2250021, 2022.

M. Cao, Z. Liu, Y. Lin, Y. Luo, S. Li, Q. Huang, H. Liu, and J. Liu. A Personalized Management Approach of OHSS: Development of a Multiphase Prediction Model and Smartphone-Based App. Frontiers in Endocrinology, 136: 911225, 2022.

P. Christianto, E. Sediyono, and I. Sembiring. Modification of Case-Based Reasoning Similarity Formula to Enhance the Performance of Smart System in Handling the Complaints of in vitro Fertilization Program Patients. Healthcare Informatics Research, 28(3): 267–275, 2022.

T. Hovorushchenko, A. Herts, and Ye. Hnatchuk. Concept of Intelligent Decision Support System in the Legal Regulation of the Surrogate Motherhood. CEUR-WS, 2488: 57–68, 2019.

T. Hovorushchenko, A. Herts, and Ye. Hnatchuk. Intelligent Agent for Support of Decision Making on Civil Law Regulation of Contract for the Provision of In Vitro Fertilization. In: Proceedings of 2020 IEEE International Scientific and Technical Conference on Computer Science and Information Technologies, pages 312–315, 2020.

C. Hickman, H. Alshubbar, J. Chambost, C. Jacques, C.-A. Pena, A. Drakeley, and T. Freour. Data sharing: using blockchain and decentralized data technologies to unlock the potential of artificial intelligence: What can assisted reproduction learn from other areas of medicine? Fertility and Sterility, 114(5): 927–933, 2020.

D.-Y. Liao. A Federated Blockchain Approach for Fertility Preservation and Assisted Reproduction in Smart Cities. Smart Cities, 5(2): 583–607, 2022.

T. Hovorushchenko, Ye. Hnatchuk, A. Herts, and O. Onyshko. Intelligent Information Technology for Supporting the Medical Decision-Making Considering the Legal Basis. CEUR-WS, 2853: 72–82, 2021.

Court decision No. 89678532, 05/18/2020, Khmelnitsky city district court of Khmelnitsky region. URL: https://youcontrol.com.ua/catalog/court-document/89678532/.

Court decision dated 02.10.2019 in case No. 686/26584/19 Khmelnytskyi City District Court of Khmelnytskyi Region. URL: https://zakononline.com.ua/court-decisions/show/84811389.

Maximum protection – services of lawyers and lawyers. Legal assistance in civil, criminal, economic, civil, administrative cases. URL: https://mzahyst.com/practice-areas/medicine-law/.

Downloads

Published

2023-05-18

How to Cite

1.
Hovorushchenko T, Hnatchuk Y, Osyadlyi V, Kapustian M, Boyarchuk A. Blockchain-Based Medical Decision Support System. JCSANDM [Internet]. 2023 May 18 [cited 2024 Apr. 20];12(03):253-74. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/18797

Issue

Section

Assurance of Information Systems’ Quality and Security

Most read articles by the same author(s)