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

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

Department of Computer Science and Engineering

University at Buffalo

Research output: Contribution to journal › Conference article › peer-review

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 language	English
Journal	CEUR Workshop Proceedings
Volume	2161
State	Published - 2018
Event	26th Italian Symposium on Advanced Database Systems, SEBD 2018 - Castellaneta Marina (Taranto), Italy Duration: Jun 24 2018 → Jun 27 2018

Cite this

@article{a3e98501bfe74dd9a2a2ea584717cdc6,

title = "An Attention-based Recurrent Neural Networks Framework for Health Data Analysis",

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{\textquoteright} 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.",

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

year = "2018",

language = "English",

volume = "2161",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

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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

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 - https://www.scopus.com/pages/publications/85051845046

M3 - Conference article

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 -

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

Abstract

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