LATENT SPACE MODELING FOR HUMAN DISEASE NETWORK WITH TEMPORAL VARIATIONS: ANALYSIS OF MEDICARE DATA

• 成果类型: Article
• 署名作者: Zhu, Guojun; Wang, Ruiyue; Li, Rong; Zhang, Sanguo; Ma, Shuangge; Qiao, Guanzhong; Mei, Hao
• 署名单位: Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Yale University; Tsinghua University; Renmin University of China
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2121
• 发表日期: 2026-03
• 页码: 364-384
• 关键词: Human disease network; latent space modeling; temporal variation; penalized estima-tion; Medicare data; neuroendocrine

摘要

Human disease network (HDN) analysis, which jointly considers a large number of diseases and focuses on their interconnections, is getting increasingly popular and can shed important insight not possessed by individual-disease-based analysis. Multiple network analysis techniques have been developed for HDNs, although new developments are still strongly needed. In this article we adopt latent space modeling, which has proven powerful in other network analysis contexts and offers unique, insightful interpretations, but has been limitedly applied in HDN analysis. Different from some other types of network analysis and some other HDN analyses (such as gene-centric ones), in this article we pay unique attention to modeling temporal variations. For this purpose, a penalization approach is developed, which can identify time regions with constant network structures (that correspond to ignorable changes) as well as those with smooth variations. The statistical and computational properties are rigorously established. With Medicare data-one of the most powerful medical claims databases-we analyze the admission records of 133 million hospital inpatient treatments from January 2008 to December 2019. Sensible findings are made on disease interconnections and clustering structures. Additionally, the temporal variations, which have not been revealed in the literature, are found to be interpretable. The analysis can provide a new way for connecting and grouping diseases and assist in understanding and planning medical resources.