标题：MTPGraph: A Data-Driven Approach to Predict Medical Risk Based on Temporal Profile Graph
作者：Zhang, Shuai; Liu, Lei; Li, Hui; Xiao, Zongshui; Cui, Lizhen
作者机构：[Zhang, Shuai; Liu, Lei; Li, Hui; Cui, Lizhen] Shandong Univ, Sch Comp Sci & Technol, Jinan, Peoples R China.; [Xiao, Zongshui] Shandong Univ, Elect 更多
会议名称：15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big Data Science and Engineering / 14th IEEE Int Symposium on Parallel and Distributed Proc with Applicat (IEEE Trustcom/BigDataSE/ISPA)
会议日期：AUG 23-26, 2016
来源：2016 IEEE TRUSTCOM/BIGDATASE/ISPA
关键词：Electronic Health Records; Temporal Feature Graphs; Medical Risk; Prediction
摘要：With the rapid development of information technologies, which facilitates the perfection of healthcare systems, a variety of clinical data is becoming available. The patient Electronic Health Records (EHR) is one of important sources in healthcare data on which conducts personalized medicine. However, it is challenging if the raw EHRs are directly used to conduct related medical prediction due to its heterogeneity, sparsity and the existence of noise. To address this issue, this paper proposes an integrative data driven medical prediction approach called Medical Temporal Profile Graph (MTPGraph). The approach consists of two parts, first of which is a unified representation for each patient raw EHRs, namely patient temporal profile graph. Secondly, based on this representation, an algorithm TRApriori to obtain temporal feature graphs is further developed which is used to reconstruct each patient temporal profiling. The generated coefficient can be efficiently used for executing medical risk prediction. Finally, we validate MTPGraph through two real world clinical scenarios. The experimental results show that the predicted performance of the approach can be improved significantly in both tasks.