Clustering temporal disease networks to assist clinical decision support systems in visual analytics of comorbidity progression
Clustering temporal disease networks to assist clinical decision support systems in visual analytics of comorbidity progression
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DOI：
10.1016/j.dss.2021.113583
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发表时间：
2021-07-07
影响因子：
7.5
通讯作者：
Gin, Andrew
中科院分区：
文献类型：
--
作者：
Lu, Yajun;Chen, Suhao;Gin, Andrew
合并症的检测和表征，即在特定患者队列中同时发生的一种以上不同疾病或疾病的存在，是一种宝贵的决策辅助工具，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的可视化分析系统，可以支持有效的模式检测和直观的可视化通过时间疾病网络（TDNs）建模的并发症进展。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，以检测显著进展变化的时间并简化TDNs的可视化。通过对艰难梭菌和中风的两个案例研究，我们证明了所提出的系统能够有效地为临床决策支持提供关于合并症进展的循证和可视化见解。
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.