Clustering temporal disease networks to assist clinical decision support systems in visual analytics of comorbidity progression

Abstract

Detection and characterization of comorbidity, the presence of more than one distinct disorder or illness concurrently occurring among a specific cohort of patients, is an invaluable decision aid and a prominent challenge in healthcare research and practice. The aim of this paper is to design a novel visual analytics system that can support efficient pattern detection and intuitive visualization of comorbidity progression modeled via temporal disease networks (TDNs). In the underlying system, we proposed two new clustering technologies—temporal clustering and disease clustering to detect the time of notable progression changes and simplify the visualization of TDNs. Through two case studies on Clostridioides Difficile and stroke, we demonstrate that the proposed system is able to provide evidence-based and visual insights regarding comorbidity progression effectively for clinical decision support.