ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks Tsung-Ting Kuo, Chun-Nan Hsu, and.

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Presentation transcript:

ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks Tsung-Ting Kuo, Chun-Nan Hsu, and Lucila Ohno-Machado pSCANNER Face-to-Face Meeting October 13, 2016 Office of the National Coordinator for Health IT

Feasible to predict outcome Hard to predict outcomes Predictive Modeling Healthcare predictive modeling Machine learning from healthcare data to predict outcomes Cross-institutional healthcare predictive modeling More generalizable models Comparative effectiveness research, biomedical discovery, patient-care, etc. Especially for large research network like pSCANNER Site 1 Site 2 Site 3 Site 4 Records = 10 Records = 760 Records = 1,500 Records = 380 Feasible to predict outcome Patients Site 1 Patient Records = 10 Hard to predict outcomes Patients

Protecting Privacy of Individuals Challenge of cross-institutional predictive modeling Improper disclosure of protected heath information (PHI) Privacy-preserving algorithms transfer models but not PHI Many methods exist [Wu et al. 2012] [Li et al. 2015] [Wang et al. 2013] [Yan et al. 2013] Site 1 Site 2 Site 3 Site 4 Share observation-level patient healthcare data Site 1 Site 2 Site 3 Site 4 Share partially-trained machine learning models Privacy-preserving predictive modeling algorithms

Risk for Existing Algorithms Site 1 Site 2 Site 3 Site 4 Central Server Centralized architecture Institutional policies Single-point-of-failure/breach Sites cannot join/leave at any time Mutable data and records Consensus/synchronization issues

The Blockchain Technology Desirable features Decentralized architecture Peer-to-peer Sites keep full control of resources No risk of single-point-of-failure Sites can join/leave freely No central server overhead No disruption of learning process Immutable audit trail Tampering is difficult Site 1 Site 2 Site 3 Site 4

ModelChain Privacy-preserving online machine learning on blockchains Transaction metadata to transfer partial models and info Proof-of-information algorithm to decide order of learning Site 2 Site 1 Model M11 Model M11 Model M22 Site 3 Model M11 Model M22 Model M33 Site 4

Summary Blockchain infrastructure as in Bitcoin ModelChain: distributed predictive models across institutes Privacy protection by exchange of model, not patient data No single-point-of-failure Verified data provenance Secure record of transactions Improved interoperability White paper and full slides: https://healthit.gov/blockchain

Lucila Ohno-Machado MD, PhD Acknowledgement The authors would like to thank Xiaoqian Jiang, PhD, and Shuang Wang, PhD for very helpful discussions All authors are funded by PCORI CDRN-1306-04819 LO-M is funded by NIH U54HL108460, UL1TR001442, and VA I01HX000982 Tsung-Ting Kuo PhD Chun-Nan Hsu PhD Lucila Ohno-Machado MD, PhD Xiaoqian Jiang PhD Shuang Wang PhD

Thank you! Questions?