An Attention-based Recurrent Neural Networks Framework for Health Data Analysis

Qiuling Suo, Fenglong Ma, Giovanni Canino, Jing Gao, Aidong Zhang, Agostino Gnasso, Giuseppe Tradigo, Pierangelo Veltri

Research output: Contribution to journalConference article

Abstract

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.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume2161
StatePublished - Jan 1 2018
Event26th Italian Symposium on Advanced Database Systems, SEBD 2018 - Castellaneta Marina (Taranto), Italy
Duration: Jun 24 2018Jun 27 2018

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Fingerprint Dive into the research topics of 'An Attention-based Recurrent Neural Networks Framework for Health Data Analysis'. Together they form a unique fingerprint.

  • Cite this

    Suo, Q., Ma, F., Canino, G., Gao, J., Zhang, A., Gnasso, A., Tradigo, G., & Veltri, P. (2018). An Attention-based Recurrent Neural Networks Framework for Health Data Analysis. CEUR Workshop Proceedings, 2161.