Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing

【Author】 Rajawat, Anand Singh; Goyal, S. B.; Bedi, Pradeep; Simoff, Simeon; Jan, Tony; Prasad, Mukesh

【Source】APPLIED SCIENCES-BASEL

【影响因子】2.838

【Abstract】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.

【Keywords】clinical information sharing; blockchain; healthcare; IoT devices; consensus model; machine learning

【发表时间】2022 NOV

【收录时间】2022-11-24

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-医疗领域