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
Year2020
Month2
Volume Number24
Issue Number2
PublisherIEEE
Pages486 - 492
ISSN2168-2194
LanguagesEnglish-United States

Last updated on 2020-12-09 at 03:43