Federated Learning Based on Extremely Sparse Series Clinic Monitoring Data

Abstract: Decentralized machine learning frameworks, e.g., federated learning, are emerging to facilitate learning with medical data under privacy protection. It is widely agreed that the establishment of an accurate and robust medical learning model requires a large number of continuous synchronous monitoring data of patients from various types of monitoring facilities. However, the clinic monitoring data are usually sparse and imbalanced with errors and time irregularity, leading to inaccurate risk prediction results. To address this issue, this paper designs a medical data resampling and balancing scheme for federated learning to eliminate model biases caused by sample imbalance and provide accurate disease risk prediction on multi-center medical data. Experimental results on a real-world clinical database MIMIC-IV demonstrate that the proposed method can improve AUC (the area under the receiver operating characteristic) from 50.1% to 62.8%, with a significant performance improvement of accuracy from 76.8% to 82.2%, compared to a vanilla federated learning artificial neural network (ANN). Moreover, we increase the model’s tolerance for missing data from 20% to 50% compared with a stand-alone baseline model.
Keywords: federate learning; time-series electronic health records (EHRs); feature engineering; imbalance sample