Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis

Abstract

Background: There is an urgent need for the development of global analytic frameworks that can perform analyses in a privacy-preserving federated environment across multiple institutions without privacy leakage. A few studies on the topic of federated medical analysis have been conducted recently with the focus on several algorithms. However, none of them have solved similar patient matching, which is useful for applications such as cohort construction for cross-institution observational studies, disease surveillance, and clinical trials recruitment. Objective:The aim of this study was to present a privacy-preserving platform in a federated setting for patient similarity learning across institutions. Without sharing patient-level information, our model can find similar patients from one hospital to another. Methods:We proposed a federated patient hashing framework and developed a novel algorithm to learn context-specific hash codes to represent patients across institutions. The similarities between patients can be efficiently computed using the resulting hash codes of corresponding patients. To avoid security attack from reverse engineering on the model, we applied homomorphic encryption to patient similarity search in a federated setting. Results:We used sequential medical events extracted from the Multiparameter Intelligent Monitoring in Intensive Care-III database to evaluate the proposed algorithm in predicting the incidence of five diseases independently. Our algorithm achieved averaged area under the curves of 0.9154 and 0.8012 with balanced and imbalanced data, respectively, in κ-nearest neighbor with κ=3. We also confirmed privacy preservation in similarity search by using homomorphic encryption. Conclusions:The proposed algorithm can help search similar patients across institutions effectively to support federated data analysis in a privacy-preserving manner.

The future of digital health with federated learning

“…Federated learning (FL) 9-11 is a learning paradigm seeking to address the problem of data governance and privacy by training algorithms collaboratively without exchanging the data itself. Originally developed for different domains, such as mobile and edge device use cases 12 , it recently gained traction for healthcare applications [13][14][15][16][17][18][19][20] . FL enables gaining insights collaboratively, e.g., in the form of a consensus model, without moving patient data beyond the firewalls of the institutions in which they reside.…”

Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications

“…The third best paper addressed the challenging task of applying learning models in data sets that are distributed across institutions. Lee et al, presented a novel privacy-preserving analytics platform for patient similarity learning in a federated setting, through a multi-hash approach for context dependent cross-institution patient representation, and incorporation of homomorphic encryption for privacy preservation [8]. This work addressed the important problem of enabling privacy-preserving learning in healthcare, where sufficient data to make inferences might be stored across a wide variety of sites.…”

“…Section editors screened this initial list for relevance to the theme and scientific quality, and they rated each paper as "keep," "pend," or "discard." Papers rated as "keep" by one of the section editors were independently reviewed and scored by section editors to yield the top 15 candidate best papers [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Criteria for scoring included innovation beyond established AI techniques, work that addressed substantial challenges in the field, and rigorous scientific evaluations.…”

“…The top-ranked 15 manuscripts underwent formal peer review by at least four internationally renowned scientists from across the world. Based on ratings and comments from peer review, section editors' recommendations, and input from the IMIA Yearbook Editorial Board, three papers [3,8,11] were chosen as 2018 best papers in the AI for Health special section of the IMIA Yearbook.…”

Medal: a patient similarity metric using medication prescribing patterns

“…The vector of features used for PSM include patient information like genetics 8 , demographics and population characteristics 9 , prescriptions and lab tests 4,10 , medical billing codes 11 , and even clinical narratives that get processed with natural language tools to extract features from the text 12 . However, the temporal dimension has not been sufficiently explored.…”