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1 Ergebnisse
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Prediction metrics [n (%)] of 3 machine learning models und..:
Aixia Guo (8808158)
;
Nikhilesh R. Mazumder (11361864)
;
Daniela P. Ladner (6310698)
.
https://figshare.com/articles/journal_contribution/Prediction_metrics_n_of_3_machine_learning_models_under_10_different_tradeoffs_for_case_of_365_days_/16548783. , 2021
Link:
https://doi.org/10.1371/journal.pone.0256428.s004
RT Journal T1
Prediction metrics [n (%)] of 3 machine learning models under 10 different tradeoffs for case of 365 days
UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_16548783&Exemplar=1&LAN=DE A1 Aixia Guo (8808158) A1 Nikhilesh R. Mazumder (11361864) A1 Daniela P. Ladner (6310698) A1 Randi E. Foraker (9239095) YR 2021 K1 Infectious Diseases K1 Virology K1 Biological Sciences not elsewhere classified K1 Mathematical Sciences not elsewhere classified K1 receiver operating curve K1 large medical center K1 electronic health records K1 laboratory test results K1 xlink "> performance K1 related diagnoses would K1 dnn model achieved K1 day mortality respectively K1 41 available variables K1 machine learning algorithms K1 dnn model outperformed K1 xlink "> K1 machine learning K1 current model K1 variables outperformed K1 laboratory measurements K1 deep learning K1 united states K1 two cases K1 time window K1 three time K1 risk prediction K1 prediction cases K1 models comprising K1 missing values K1 logistic regression K1 leading cause K1 effects millions K1 effectively treat K1 different number K1 current standard K1 corresponding aucs K1 continuous variables K1 categorical variables K1 baseline features K1 another assumption K1 alkaline phosphatase K1 alanine aminotransferase K1 4 variables JF https://figshare.com/articles/journal_contribution/Prediction_metrics_n_of_3_machine_learning_models_under_10_different_tradeoffs_for_case_of_365_days_/16548783 LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0256428.s004 DO https://doi.org/10.1371/journal.pone.0256428.s004 SF ELIB - SuUB Bremen
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