Federated Transfer Learning for Early Detection of Multi-Organ Failure: A Scalable Predictive Healthcare Framework

Abstract

The early Identification of multi-organ disorder (MOF) is critical for improving patient results in critical care settings. This paper presents a novel predictive healthcare framework depends on Federated Transfer based Learning (FTL), designed in addressing the challenges of early MOF detection over multiple organs, including the heart, liver, kidney, and lungs. By utilizing federated learning, the proposed model leveraged decentralized patient information from diverse medical institutions while ensuring data's privacy and security. Transfer learning will be employed to enhance model accuracy by transferring knowledge from pretrained models over similar medical domains. The scalable framework will be optimized for real-time analysis, giving robust predictions that assisting healthcare professionals for timely decision-making. The Extensive experiments using real-world medical datasets shows the model's superior performances in terms of prediction accuracy level, sensitivity rate, and generalizability. This research highlighted the potential of FTL to revolutionize predictive healthcare by enabling collaborative, data-privacy preserving, and scalable solutions for MOF identification.

Ähnliche Arbeiten