The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm

Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions

OBJECTIVE

We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions.

MATERIALS AND METHODS

We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution.

RESULTS

The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline.

DISCUSSION

The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows.

CONCLUSION

Blockchain technology can be used to support federated data analysis among multiple institutions.

Quorum-based model learning on a blockchain hierarchical clinical research network using smart contracts

BACKGROUND

Collaborative privacy-preserving modeling across several healthcare institutions allows for the construction of more generalizable predictive models while protecting patient privacy.

OBJECTIVE

We aim at addressing the site availability issue on a hierarchical network by designing an immutable/transparent/source-verifiable quorum mechanism.

METHODS

We developed an approach to combine a hierarchical learning algorithm, a novel Proof-of-Quorum (PoQ) consensus protocol, and a design of blockchain smart contracts. We constructed QuorumChain as an example and evaluated the scenarios of site-unavailability during the initialization and/or iteration phases of the modeling process on three healthcare/genomic datasets.

RESULTS

When one or more sites would become unavailable, HierarchicalChain could not function, whereas QuorumChain improved predictive correctness significantly (the full Area Under the receiver operating characteristic Curve, or AUC, improved from 0.068 to 0.441, all with p-values < 0.001).

CONCLUSION

By constructing a quorum to continue the modeling process, QuorumChain possesses the capability to tackle the situation of sites being unavailable. It inherits the capability of learning on network-of-networks, improves learning continuity, and provides data/software immutability, transparency, and provenance, which can be important in expediting clinical research.

Functional genomics data: privacy risk assessment and technological mitigation

The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.

CertificateChain: decentralized healthcare training certificate management system using blockchain and smart contracts

Objective

Managing training certificates is an important issue in research that can lead to serious issues if not addressed properly. For institutions that currently do not have a dedicated management system for these training certificates, a central database is the most typical solution. However, such a system suffers from several risks, such as a single-point-of-failure.

Materials and Methods

To address this issue, we developed and evaluated CertificateChain, a decentralized training certificate management system by using peer-to-peer blockchain and automated smart contracts. We developed an efficient certificate dividing-and-merging algorithm to overcome the transaction size limit on blockchain.

Results

We performed experiments on the system to evaluate its performance, then created a web app and tested the system in a real-world scenario. CertificateChain scaled linearly in terms of time compared with the total number of certificates added and could be quickly queried for existing data stored on-chain.

Discussion

CertificateChain was able to store and retrieve the training certificates on the blockchain network, with limitations including a comparative analysis of other systems, evaluation of different consensus protocols, examining certificates off-chain, a thorough comparison with a centralized system, and the extension to the main public Ethereum network.

Conclusion

We believe that these results indicate that blockchain technology could be a viable decentralized alternative to traditional databases in this use case. Our software is publicly available at: https://doi.org/10.5281/zenodo.6257094.

In many research scenarios, certifications are required for data access requests. Institutions must manage the relevant certificates to avoid potentially serious scenarios that could impede research. Most existing systems suffer from risks such as single-point-of-failure, a scenario in which an entire system can be rendered ineffective with the failure of only one node in the network. To solve this problem, we developed CertificateChain, a decentralized certificate management system that adopted blockchain and smart contract (programs running on blockchain) technology and stores the certificates on-chain. To evaluate the system’s performance, we performed experiments on it by storing Collaborative Institutional Training Initiative (CITI) certificate files to test its scalability and speed, as well as real-world testing using an accompanying web app. We found that in terms of time, the system scaled linearly, and could quickly be searched for any existing certificates. The limitations include the evaluation of other blockchain consensus protocols, verification of certificate authenticity before and after uploading, the scalability of upload file size, as well as an in-depth comparison to existing centralized systems. After developing and evaluating the system, we believe that CertificateChain shows potential to be a viable decentralized alternative for existing centralized systems.

Blockchain-Based Digital Twins Collaboration for Smart Pandemic Alerting: Decentralized COVID-19 Pandemic Alerting Use Case

Emerging technologies such as digital twins, blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) play a vital role in driving the industrial revolution in all domains, including the healthcare sector. As a result of COVID-19 pandemic outbreak, there is a significant need for medical cyber-physical systems to adopt these emerging technologies to combat COVID-19 paramedic crisis. Also, acquiring secure real-time data exchange and analysis across multiple participants is essential to support the efforts against COVID-19. Therefore, we have introduced a blockchain-based collaborative digital twins framework for decentralized epidemic alerting to combat COVID-19 and any future pandemics. The framework has been proposed to bring together the existing advanced technologies (i.e., blockchain, digital twins, and AI) and then provide a solution to decentralize epidemic alerting to combat COVID-19 outbreaks. Also, we have described how the conceptual framework can be applied in the decentralized COVID-19 pandemic alerting use case.

Is blockchain ready for orthopaedics? A systematic review

INTRODUCTION

The unique attributes of distributed ledger blockchain systems including robust security, immutability, transparency, and decentralisation, make them highly suitable solutions for many healthcare-related problems. This review examines the potential applications for blockchain technology in the field of orthopaedics, by taking a systematic approach to the evolving blockchain literature and mapping potential use cases against the current needs of orthopaedic practice.

METHOD

A literature search was performed using Pubmed, EMBASE, OVID and the Cochrane library with the primary aim of identifying detailed accounts of blockchain solutions and use cases in healthcare. These articles were then reviewed and mapped against current orthopaedic practice to illustrate applications specific to that specialty.

RESULTS

One hundred and forty-one papers were identified which described case studies, simulations, or detailed proposals of blockchain solutions in healthcare. Most studies described blockchain solutions at the simulated or prototype testing phase, with only 10 case studies describing blockchains in "real-world" use. The most frequently cited use cases for blockchain technology involved the storage, security and sharing of electronic medical records. Other blockchain solutions focused on the "Internet of Things", research, COVID 19, supply chains and radiology. There were no solutions focusing specifically on orthopaedics. Many of the described blockchain solutions had considerable scope for application in orthopaedic practice however, providing the potential for greater inter-institutional collaboration, cross border data exchange, enhanced patient participation, and more robust and transparent research practices.

CONCLUSION

Blockchain solutions for healthcare are increasing in number and scope and have multiple applications relevant to orthopaedic practice. The orthopaedic community needs to be aware of this innovative and growing field of computer science so that surgeons can leverage the power of blockchain safely for the future of orthopaedics.

Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case

Digital twins (DTs) is a promising technology in the revolution of the industry and essential for Industry 4.0. DTs play a vital role in improving distributed manufacturing, providing up-to-date operational data representation of physical assets, supporting decision-making, and avoiding the potential risks in distributed manufacturing systems. Furthermore, DTs need to collaborate within distributed manufacturing systems to predict the risks and reach consensus-based decision-making. However, DTs collaboration suffers from single failure due to attack and connection in a centralized manner, data interoperability, authentication, and scalability. To overcome the above challenges, we have discussed the major high-level requirements for the DTs collaboration. Then, we have proposed a conceptual framework to fulfill the DTs collaboration requirements by using the combination of blockchain, predictive analysis techniques, and DTs technologies. The proposed framework aims to empower more intelligence DTs based on blockchain technology. In particular, we propose a concrete ledger-based collaborative DTs framework that focuses on real-time operational data analytics and distributed consensus algorithms. Furthermore, we describe how the conceptual framework can be applied using smart transportation system use cases, i.e., smart logistics and railway predictive maintenance. Finally, we highlighted the future direction to guide interested researchers in this interesting area.