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Model performance by metrics of 5-fold cross-validation mea..:

Aixia Guo (8808158) ; Sakima Smith (11426557) ; Yosef M. Khan (9239089)..
https://figshare.com/articles/dataset/Model_performance_by_metrics_of_5-fold_cross-validation_mean_std_/16612378. , 2021

Link: https://doi.org/10.1371/journal.pone.0239007.t004

RT Journal
T1 <Title>Model performance by metrics of 5-fold cross-validation mean (std)</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_16612378&Exemplar=1&LAN=DE
				
A1 Aixia Guo (8808158)
A1 Sakima Smith (11426557)
A1 Yosef M. Khan (9239089)
A1 James R. Langabeer II (9239092)
A1 Randi E. Foraker (9239095)
YR 2021
K1 Medicine
K1 Biotechnology
K1 Infectious Diseases
K1 Information Systems not elsewhere classified
K1 short term memory
K1 series cardiovascular health
K1 receiver operator curve
K1 numerical embedding vectors
K1 na &# 239
K1 fold cross validation
K1 encouraging appropriate surveillance
K1 deep neural networks
K1 data would allow
K1 predict cardiac dysrhythmias
K1 monitor outpatient prevention
K1 model &# 8211
K1 best prediction performance
K1 auroc ): 0
K1 lstm model outperformed
K1 series cvh measures
K1 outpatient setting
K1 lstm model
K1 predict cd
K1 xlink ">
K1 united states
K1 random forest
K1 new strategies
K1 longitudinal research
K1 logistic regression
K1 influential feature
K1 guideline advantage
K1 growing problem
K1 findings may
K1 earlier diagnosis
K1 considerable morbidity
K1 average area
K1 affect millions
JF https://figshare.com/articles/dataset/Model_performance_by_metrics_of_5-fold_cross-validation_mean_std_/16612378
LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0239007.t004
DO https://doi.org/10.1371/journal.pone.0239007.t004
SF ELIB - SuUB Bremen

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