Distributed information fusion for secure healthcare

Abstract

Recent years have seen a significant increase in the demand for cutting-edge healthcare systems. With the rising potential of artificial intelligence and big data technology, all sectors, especially the healthcare sector, have been greatly supported. Huge amounts of privacy-sensitive clinical data are being generated from several sources. While processing these enormous amounts of diverse healthcare data, the problem is that the data are heterogeneous. The data vary with respect to the patient population, environment, data source, size, complexity, medical procedures, and treatment protocols at individual medical centers. This creates the need for a central knowledge base in the healthcare setting. Federated learning-based fusion techniques can turn out to be beneficial to acquire knowledge from these distributed data. This will bring the distributed data together into a single view that can help hospitals and health workers to obtain new insights and helps secure patients' personal information and safeguards them from information leakage. If the distribution of data among the classes is skewed or biased, the distribution is said to be imbalanced. This chapter discusses problems associated with imbalanced and heterogeneous healthcare data and their effects on machine learning models and proposes methods to improve data fairness in a distributed healthcare system using federated learning