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

arXiv: https://arxiv.org/abs/1802.01746 · HTML: https://mlsanity.com/paper/1802.01746/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 7 of 7)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning](https://mlsanity.com/paper/2202.02817/) (2022)
- [A Review on Building Blocks of Decentralized Artificial Intelligence](https://mlsanity.com/paper/2402.02885/) (2024)
- [A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors](https://mlsanity.com/paper/2211.10713/) (2022)
- [Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum](https://mlsanity.com/paper/2207.14584/) (2022)
- [When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain](https://mlsanity.com/paper/2406.04743/) (2024)
- [Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach](https://mlsanity.com/paper/2508.07505/) (2025)

## Abstract (arXiv)

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 Blockchain 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).