Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum
This addresses the need for more efficient and secure health care data systems, though it appears incremental as it builds on existing decentralized and machine learning concepts.
The paper tackled the problem of centralized electronic health record (EHR) systems lacking transparency and efficiency by proposing a decentralized machine learning approach using distributed ledgers, resulting in up to 60% reduced machine learning time and consensus latency below 8 seconds.
The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.