MTPGraph: A Data-Driven Approach to Predict Medical Risk Based on Temporal Profile Graph

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.