Title: Highrisk Prediction from Electronic Medical Records via Deep Attention Networks,
Abstract: Predicting highrisk vascular diseases is a significant issue in the medical
domain. Most predicting methods predict the prognosis of patients from
pathological and radiological measurements, which are expensive and require
much time to be analyzed. Here we propose deep attention models that predict
the onset of the high risky vascular disease from symbolic medical histories
sequence of hypertension patients such as ICD-10 and pharmacy codes only,
Medical History-based Prediction using Attention Network (MeHPAN). We
demonstrate two types of attention models based on 1) bidirectional gated
recurrent unit (R-MeHPAN) and 2) 1D convolutional multilayer model (C-MeHPAN).
Two MeHPAN models are evaluated on approximately 50,000 hypertension patients
with respect to precision, recall, f1-measure and area under the curve (AUC).
Experimental results show that our MeHPAN methods outperform standard
classification models. Comparing two MeHPANs, R-MeHPAN provides more better
discriminative capability with respect to all metrics while C-MeHPAN presents
much shorter training time with competitive accuracy.