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The study features and feature description:

Aixia Guo (8808158) ; Nikhilesh R. Mazumder (11361864) ; Daniela P. Ladner (6310698).
https://figshare.com/articles/journal_contribution/The_study_features_and_feature_description_/16548771. , 2021

Link: https://doi.org/10.1371/journal.pone.0256428.s002

RT Journal
T1 <Title>The study features and feature description</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_16548771&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/The_study_features_and_feature_description_/16548771
LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0256428.s002
DO https://doi.org/10.1371/journal.pone.0256428.s002
SF ELIB - SuUB Bremen

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