Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality

TL;DR

This paper explores federated learning for medical in-hospital mortality prediction, demonstrating it can achieve performance comparable to centralized models while preserving patient privacy.

Contribution

It demonstrates the effectiveness of federated learning in training predictive models on distributed medical data without compromising privacy.

Findings

Federated learning achieves similar accuracy to centralized models.

Distributed training maintains patient privacy effectively.

Guidelines for implementing federated learning in healthcare.

Abstract

Machine learning models can be used for pattern recognition in medical data in order to improve patient outcomes, such as the prediction of in-hospital mortality. Deep learning models, in particular, require large amounts of data for model training. However, the data is often collected at different hospitals and sharing is restricted due to patient privacy concerns. In this paper, we aimed to demonstrate the potential of distributed training in achieving state-of-the-art performance while maintaining data privacy. Our results show that training the model in the federated learning framework leads to comparable performance to the traditional centralised setting. We also suggest several considerations for the success of such frameworks in future work.