In:
Concurrency and Computation: Practice and Experience, Wiley, Vol. 32, No. 14 ( 2020-07-25)
Abstract:
There is a large amount of historical data of the patient's hospitalization named the electronic health records (EHRs), but the data are not fully utilized for great challenges as poor quality, high dimension, and so on. Previous studies have primarily used machine learning methods that rely heavily on manual extraction of features. Recently, many deep learning approaches are applied to predictive model of EHRs. Recurrent neural networks (RNN) are often used to model EHR data, but RNN performance degrades in the face of large sequence lengths. To solve these challenges, we develop a long short‐term memory with attention mechanism for mortality prediction. The dataset used in this article is the Medical Information Mart for Intensive Care III, which contains comprehensive clinical data for the patients. The experimental results demonstrate that the predicted results can be effectively interpreted using the attention mechanism. Compared with other baseline models, our model improves the accuracy of prediction, and helps doctors reduce the average diagnostic
time.
Type of Medium:
Online Resource
ISSN:
1532-0626
,
1532-0634
Language:
English
Publisher:
Wiley
Publication Date:
2020
detail.hit.zdb_id:
2052606-4
SSG:
11
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