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.
An Attention-based Recurrent Neural Networks Framework for Health Data Analysis
Veltri P.
2018-01-01
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.File in questo prodotto:
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