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  ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private

  Blockchain Networks

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  Tsung-Ting Kuo, PhD, Chun-Nan Hsu, PhD, and Lucila Ohno-Machado, MD, PhD


  Health System Department of Biomedical Informatics, University of California San Diego, La Jolla, CA


  Division of Health Services Research & Development, VA San Diego Healthcare System


  Cross-institutional healthcare predictive modeling can accelerate research and facilitate quality improvement

  initiatives, and thus is important for national healthcare delivery priorities. For example, a model that predicts

  risk of re-admission for a particular set of patients will be more generalizable if developed with data from

  multiple institutions. While privacy-protecting methods to build predictive models exist, most are based on a

  centralized architecture, which presents security and robustness vulnerabilities such as single-point-of-failure

  (and single-point-of-breach) and accidental or malicious modification of records. In this article, we describe a

  new framework, ModelChain, to adapt Blockchain technology for privacy-preserving machine learning. Each

  participating site contributes to model parameter estimation without revealing any patient health information

  (i.e., only model data, no observation-level data, are exchanged across institutions). We integrate privacy-

  preserving online machine learning with a private block chain network, apply transaction metadata to

  disseminate partial models, and design a new proof-of-information algorithm to determine the order of the

  online learning process. We also discuss the benefits and potential issues of applying Blockchain technology to

  solve the privacy-preserving healthcare predictive modeling task and to increase interoperability between

  institutions, to support the Nationwide Interoperability Roadmap and national healthcare delivery priorities

  such as Patient-Centered Outcomes Research (PCOR).






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