| Autor: |
Yu R, Zheng Y, Zhang R, Jiang Y, Poon CCY |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2020 Feb; Vol. 24 (2), pp. 486-492. Date of Electronic Publication: 2019 May 13. |
| DOI: |
10.1109/JBHI.2019.2916667 |
| Abstrakt: |
Estimating 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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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