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FedScore: A privacy-preserving framework for federated scoring system development.

Siqi Li¹, Yilin Ning¹, Marcus Eng Hock Ong²

¹Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.

Journal of Biomedical Informatics

|September 3, 2023

Summary

FedScore enables privacy-preserving federated learning for generating accurate scoring systems across multiple institutions. This framework demonstrates good generalizability and stability for collaborative research.

Keywords:

Clinical decision making Distributed algorithm Electronic health record Electronic medical record Federated learning Scoring system

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Area of Science:

Federated Learning
Health Informatics
Collaborative Research

Background:

Cross-institutional collaborations are crucial for developing robust scoring systems.
Existing methods often face challenges with data privacy and siloed information.

Purpose of the Study:

To introduce FedScore, a novel privacy-preserving federated learning framework.
To facilitate the generation of scoring systems across multiple sites for enhanced collaboration.

Main Methods:

FedScore incorporates five modules: federated variable ranking, transformation, score derivation, model selection, and evaluation.
A hypothetical global scoring system for mortality prediction was developed using 10 simulated sites.
Performance was compared against local and centralized scoring systems.

Main Results:

The FedScore model achieved an average Area Under the Curve (AUC) of 0.763 (SD 0.020).
FedScore demonstrated promising accuracy and stability, closely approaching the performance of a pooled model.
Its standard deviation was lower than most locally generated models.

Conclusions:

FedScore is a viable privacy-preserving tool for generating scoring systems.
The framework shows potential for good generalizability in multi-site research.