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A comprehensive EHR timeseries pre-training benchmark:

, In: Proceedings of the Conference on Health, Inference, and Learning,

McDermott, Matthew ; Nestor, Bret ; Kim, Evan... - p. 257-278 , 2021

Link: https://dl.acm.org/doi/10.1145/3450439.3451877

RT 
T1 <cTitle>Proceedings of the Conference on Health, Inference, and Learning</cTitle>: T1 <Title>A comprehensive EHR timeseries pre-training benchmark</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3451877&Exemplar=1&LAN=DE
				
A1 McDermott, Matthew
A1 Nestor, Bret
A1 Kim, Evan
A1 Zhang, Wancong
A1 Goldenberg, Anna
A1 Szolovits, Peter
A1 Ghassemi, Marzyeh
PB ACM
YR 2021
K1 EHR
K1 benchmarks
K1 electronic health records
K1 multi-task learning
K1 neural networks
K1 pre-training
K1 timeseries
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Multi-task learning
K1 Machine learning approaches
K1 Neural networks
K1 Theory of computation
K1 Theory and algorithms for application domains
K1 Machine learning theory
K1 Semi-supervised learning
K1 Applied computing
K1 Life and medical sciences
K1 Bioinformatics
K1 Learning latent representations
SP 257
OP 278
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3450439.3451877
DO https://dl.acm.org/doi/10.1145/3450439.3451877
SF ELIB - SuUB Bremen

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