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1 Ergebnisse
1
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
Model performance by metrics of 5-fold cross-validation mean (std)
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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