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KGDAL : knowledge graph guided double attention LSTM for..:
, In:
Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
,
Liu, Lucas Jing
;
Ortiz-Soriano, Victor
;
Neyra, Javier A.
. - p. 1-10 , 2021
Link:
https://dl.acm.org/doi/10.1145/3459930.3469513
RT T1 <cTitle>Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics</cTitle>: T1 <Title>KGDAL : knowledge graph guided double attention LSTM for rolling mortality prediction for AKI-D patients</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3469513&Exemplar=1&LAN=DE A1 Liu, Lucas Jing A1 Ortiz-Soriano, Victor A1 Neyra, Javier A. A1 Chen, Jin PB ACM YR 2021 K1 attention mechanism K1 deep learning K1 knowledge graph K1 rolling mortality prediction K1 Applied computing K1 Life and medical sciences K1 Health informatics K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks SP 1 OP 10 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3459930.3469513 DO https://dl.acm.org/doi/10.1145/3459930.3469513 SF ELIB - SuUB Bremen
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