Paper Info
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.
Year
Venue
Field
2017
arXiv: Learning
Pharmacy,Medical history,Artificial intelligence,Medical record,Recall,Discriminative model,Radiological weapon,Mathematics,Machine learning
DocType
Volume
Citations 
Journal
abs/1712.00010
2
PageRank 
References 
Authors
0.36
8
6
Name
Order
Citations
PageRank
You Jin Kim120.70
Yun-Geun Lee220.36
Jeong-Whun Kim391.61
Jin Joo Park421.04
Borim Ryu520.70
Jung-Woo Ha621625.36