TY - JOUR
T1 - An Attention-based Recurrent Neural Networks Framework for Health Data Analysis
AU - Suo, Qiuling
AU - Ma, Fenglong
AU - Canino, Giovanni
AU - Gao, Jing
AU - Zhang, Aidong
AU - Gnasso, Agostino
AU - Tradigo, Giuseppe
AU - Veltri, Pierangelo
N1 - Publisher Copyright:
© 2018 CEUR-WS. All rights reserved.
PY - 2018
Y1 - 2018
N2 - In this paper we focus on prediction of health status of patients from the historical Electronic Health Records (EHR). We propose a multi-task framework that can monitor the multiple status of diagnoses. Patients’ historical records are fed into a Recurrent Neural Network (RNN) which memorizes all the past visit information, and then a task-specific layer is trained to predict multiple diagnoses. Experimental results show that prediction accuracy is reliable if compared to widely used approaches 1.
AB - In this paper we focus on prediction of health status of patients from the historical Electronic Health Records (EHR). We propose a multi-task framework that can monitor the multiple status of diagnoses. Patients’ historical records are fed into a Recurrent Neural Network (RNN) which memorizes all the past visit information, and then a task-specific layer is trained to predict multiple diagnoses. Experimental results show that prediction accuracy is reliable if compared to widely used approaches 1.
UR - http://www.scopus.com/inward/record.url?scp=85051845046&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051845046&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85051845046
SN - 1613-0073
VL - 2161
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 26th Italian Symposium on Advanced Database Systems, SEBD 2018
Y2 - 24 June 2018 through 27 June 2018
ER -