All-cause mortality prediction in T2D patients with iTirps

Abstract

Mortality in the type II diabetic elderly population can sometimes be prevented through intervention, for which risk assessment through predictive modeling is required. Since Electronic Health Records data are typically heterogeneous and sparse, the use of Temporal Abstraction and time intervals mining to discover frequent Time Intervals Related Patterns (TIRPs) is employed. While TIRPs are used as features for a predictive model, the temporal relations between them in general, and among each TIRP's instances are not represented. We introduce a novel TIRP based representation called integer-TIRP (iTirp) in which the TIRPs become channels containing values that represent the TIRP instances that were detected at each time point. Then the iTirp representation is fed into a Deep Learning architecture, that learns this kind of temporal relations, using a Recurrent Neural Network or a Convolutional Neural Network. Additionally, a predictive committee is introduced in which raw data and iTirp data are concatenated as inputs. Our results show that iTirps based models outperform the use of deep learning with raw data, resulting in 82% AUC.