Unknown

Dataset Information

0

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 ?-nearest neighbor with ?=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.

SUBMITTER: Lee J 

PROVIDER: S-EPMC5924379 | BioStudies | 2018-01-01

REPOSITORIES: biostudies

Similar Datasets

2020-01-01 | S-EPMC7755539 | BioStudies
2019-01-01 | S-EPMC6411680 | BioStudies
2017-01-01 | S-EPMC5547495 | BioStudies
2020-01-01 | S-EPMC7671849 | BioStudies
1000-01-01 | S-EPMC4731770 | BioStudies
2017-01-01 | S-EPMC5547453 | BioStudies
2016-01-01 | S-EPMC4985613 | BioStudies
2020-01-01 | S-EPMC7261120 | BioStudies
2017-01-01 | S-EPMC5547444 | BioStudies
2018-01-01 | S-EPMC5859787 | BioStudies