A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit. (3rd November 2019)

• Record Type: Journal Article
• Authors: Xia, Jing; Pan, Su; Zhu, Min; Cai, Guolong; Yan, Molei; Su, Qun; Yan, Jing; Ning, Gangmin
• Other Names: Duggento Andrea Guest Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2019(2019)
• Publication Date: 2019-11-03
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2019/8152713
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : 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.