Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
This addresses privacy concerns in healthcare data sharing for hospitals, but is incremental as it applies an existing federated learning framework to a medical task.
The paper tackled the problem of training deep learning models for in-hospital mortality prediction without sharing sensitive patient data across hospitals, and found that federated learning achieved performance comparable to centralized training.
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.