Using a Multi-task Recurrent Neural Network with Attention Mechanisms to Predict Hospital Mortality of Patients
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AbstractEstimating hospital mortality of patients is important in assisting clinicians to make decisions and hospital providers to allocate resources. This paper proposed a multi-task recurrent neural network with attention mechanisms to predict patients’ hospital mortality, using reconstruction of patients’ physiological time series as an auxiliary task. Experiments were conducted on a large public electronic health record database, i.e., MIMIC-III. Fifteen physiological measurements during the first 24 h of critical care were used to predict death before hospital discharge. Compared with the conventional simplified acute physiology score (SAPS-II), the proposed multi-task learning model achieved better sensitivity (0.503 ± 0.020 versus 0.365 ± 0.021), when predictions were made based on the same 24-h observation period. The multi-task learning model is recommended to be updated daily with at least a 6-h observation period, in order for it to perform similarly or better than the SAPS-II. In the future, the need for intervention can be considered as another task to further optimize the performance of the multi-task learning model.
All Author(s) ListRuoxi Yu, Yali Zheng, Ruikai Zhang, Yuqi Jiang, Carmen C.Y. Poon
Journal nameIEEE Journal of Biomedical and Health Informatics
Volume Number24
Issue Number2
Pages486 - 492
LanguagesEnglish-United States

Last updated on 2021-10-05 at 01:55