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Effective monitoring of degenerative patient conditions is crucial for many clinical decision-making problems. Leveraging the nowadays data-rich environments in many clinical settings, in this paper, we propose a novel clinical data fusion framework that can build a contemporaneous health index (CHI) for degenerative disease monitoring to quantify the severity of deterioration process over time. Our framework specifically exploits the monotonic progression patterns of the target degenerative disease conditions such as the Alzheimer’s disease (AD) and articulate these patterns with a systematic optimization formulation. Further, to address the patient heterogeneity, we integrate CHI with dictionary learning to build sets of overcomplete bases to represent the personalized models efficiently. Numerical performances on two real-world applications show the promising capability of the proposed DL-CHI model.

DL-CHI: a dictionary learning-based contemporaneous health index for degenerative disease monitoring