Information Theory for Medical Data Fusion

Abstract

Data fusion is the process of integrating multiple heterogeneous data sources to produce more accurate, comprehensive, and useful information than any single source alone. For the medical data fusion, this information may come from imaging, genomic data, clinical records, and physiological signals. It has emerged as a cornerstone of modern precision medicine. Information theory provides a rigorous mathematical framework for quantifying uncertainty, measuring information gain, and can thus be used to optimize fusion strategies across diverse clinical contexts. This paper presents a review of information-theoretic approaches suitable for medical data fusion, covering: fundamental concepts (entropy, mutual information, Kullback-Leibler divergence, rate-distortion), theoretical frameworks (information bottleneck, transfer entropy, partial information decomposition), and their application across major clinical domains. We synthesize recent advances in fusion-based architectures, discuss the critical challenges of uncertainty quantification, and provide practical guidelines for implementing information-theoretic fusion in clinical settings. Through a systematic analysis, we identify key challenges, including parameter sensitivity, missing modalities, and clinical interpretability, to outline promising directions for future research. This review aims to provide clinicians, researchers, and developers with a comprehensive understanding of how information theory can transform medical data for improved diagnostic accuracy, prognostic precision, and personalized patient care.