A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit
Joint Authors
Xia, Jing
Yan, Molei
Ning, Gangmin
Pan, Su
Zhu, Min
Cai, Guolong
Su, Qun
Yan, Jing
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In intensive care unit (ICU), it is essential to predict the mortality of patients and mathematical models aid in improving the prognosis accuracy.
Recently, recurrent neural network (RNN), especially long short-term memory (LSTM) network, showed advantages in sequential modeling and was promising for clinical prediction.
However, ICU data are highly complex due to the diverse patterns of diseases; therefore, instead of single LSTM model, an ensemble algorithm of LSTM (eLSTM) is proposed, utilizing the superiority of the ensemble framework to handle the diversity of clinical data.
The eLSTM algorithm was evaluated by the acknowledged database of ICU admissions Medical Information Mart for Intensive Care III (MIMIC-III).
The investigation in total of 18415 cases shows that compared with clinical scoring systems SAPS II, SOFA, and APACHE II, random forests classification algorithm, and the single LSTM classifier, the eLSTM model achieved the superior performance with the largest value of area under the receiver operating characteristic curve (AUROC) of 0.8451 and the largest area under the precision-recall curve (AUPRC) of 0.4862.
Furthermore, it offered an early prognosis of ICU patients.
The results demonstrate that the eLSTM is capable of dynamically predicting the mortality of patients in complex clinical situations.
American Psychological Association (APA)
Xia, Jing& Pan, Su& Zhu, Min& Cai, Guolong& Yan, Molei& Su, Qun…[et al.]. 2019. A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1130726
Modern Language Association (MLA)
Xia, Jing…[et al.]. A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1130726
American Medical Association (AMA)
Xia, Jing& Pan, Su& Zhu, Min& Cai, Guolong& Yan, Molei& Su, Qun…[et al.]. A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1130726
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130726