A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain
This work addresses trust and automation problems in machine learning for domains like healthcare, though it appears incremental as it combines existing technologies.
The paper tackles the issues of data trustability and process automation in machine learning by proposing a unified framework that integrates blockchain for immutable data storage and smart contracts for automation, demonstrating its application with association rule mining on opioid prescription data to aid in combating the opioid crisis.
Traditional machine learning algorithms use data from databases that are mutable, and therefore the data cannot be fully trusted. Also, the machine learning process is difficult to automate. This paper proposes building a trustable machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts are used to automate the machine learning process. This paper makes three contributions. First, it establishes a link between machine learning technology and blockchain technology. Previously, machine learning and blockchain have been considered two independent technologies without an obvious link. Second, it proposes a unified analytical framework for trustable machine learning by using blockchain technology. This unified framework solves both the trustability and automation issues in machine learning. Third, it enables a computer to translate core machine learning implementation from a single thread on a single machine to multiple threads on multiple machines running with blockchain by using a unified approach. The paper uses association rule mining as an example to demonstrate how trustable machine learning can be implemented with blockchain, and it shows how this approach can be used to analyze opioid prescriptions to help combat the opioid crisis.