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.

共病的检测和表征，即在特定的患者队列中同时发生不止一种不同的疾病或疾病，是一种宝贵的决策帮助，也是医疗保健研究和实践中的一个突出挑战。本文的目的是设计一种新颖的可视化分析系统，该系统可以支持通过时间疾病网络 (TDN) 建模的合并症进展的有效模式检测和直观可视化。在底层系统中，我们提出了两种新的聚类技术——时间聚类和疾病聚类，以检测显着进展变化的时间并简化 TDN 的可视化。通过艰难梭菌和中风的两个案例研究，