A compact edge EHR with a random forest model estimates hemoglobin from fingernail images with RMSE 1.969 g/dL and 79.2% sensitivity on a public 250-image dataset.
BEdgeHealth: A Decentralized Architecture for Edge-based IoMT Networks Using Blockchain
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abstract
The healthcare industry has witnessed significant transformations in e-health services by using mobile edge computing (MEC) and blockchain to facilitate healthcare operations. Many MEC-blockchain-based schemes have been proposed, but some critical technical challenges still remain, such as low quality of services (QoS), data privacy and system security vulnerabilities. In this paper, we propose a new decentralized health architecture, called BEdgeHealth that integrates MEC and blockchain for data offloading and data sharing in distributed hospital networks. First, a data offloading scheme is proposed where mobile devices can offload health data to a nearby MEC server for efficient computation with privacy awareness. Moreover, we design a data sharing scheme which enables data exchanges among healthcare users by leveraging blockchain and interplanetary file system. Particularly, a smart contract-based authentication mechanism is integrated with MEC to perform decentralized user access verification at the network edge without requiring any central authority. The real-world experiment results and evaluations demonstrate the effectiveness of the proposed BEdgeHealth architecture in terms of improved QoS with data privacy and security guarantees, compared to the existing schemes.
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Design of an Edge-based Portable EHR System for Anemia Screening in Remote Health Applications
A compact edge EHR with a random forest model estimates hemoglobin from fingernail images with RMSE 1.969 g/dL and 79.2% sensitivity on a public 250-image dataset.