Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing
Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multip...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/10795 |
| _version_ | 1827647414963535872 |
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| author | Anand Singh Rajawat S. B. Goyal Pradeep Bedi Simeon Simoff Tony Jan Mukesh Prasad |
| author_facet | Anand Singh Rajawat S. B. Goyal Pradeep Bedi Simeon Simoff Tony Jan Mukesh Prasad |
| author_sort | Anand Singh Rajawat |
| collection | DOAJ |
| description | Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results. |
| first_indexed | 2024-03-09T19:17:49Z |
| format | Article |
| id | doaj.art-1c163492a1374385b33c6fbb6041da93 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:17:49Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1c163492a1374385b33c6fbb6041da932023-11-24T03:32:57ZengMDPI AGApplied Sciences2076-34172022-10-0112211079510.3390/app122110795Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information SharingAnand Singh Rajawat0S. B. Goyal1Pradeep Bedi2Simeon Simoff3Tony Jan4Mukesh Prasad5School of Computer Sciences & Engineering, Sandip University, Nashik 422213, IndiaFaculty of Information Technology, City University, Petaling Jaya 46100, MalaysiaSchool of Computing Science and Engineering, Galgotias University, Greater Noida 203201, IndiaSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney 2751, AustraliaCentre for Artificial Intelligence Research and Optimization, Design and Creative Technology Vertical, Torrens University, Sydney 2007, AustraliaSchool of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, AustraliaLarge-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results.https://www.mdpi.com/2076-3417/12/21/10795clinical information sharingblockchainhealthcareIoT devicesconsensus modelmachine learning |
| spellingShingle | Anand Singh Rajawat S. B. Goyal Pradeep Bedi Simeon Simoff Tony Jan Mukesh Prasad Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing Applied Sciences clinical information sharing blockchain healthcare IoT devices consensus model machine learning |
| title | Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing |
| title_full | Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing |
| title_fullStr | Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing |
| title_full_unstemmed | Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing |
| title_short | Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing |
| title_sort | smart scalable ml blockchain framework for large scale clinical information sharing |
| topic | clinical information sharing blockchain healthcare IoT devices consensus model machine learning |
| url | https://www.mdpi.com/2076-3417/12/21/10795 |
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