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Fifty 1-gram words with high features were extracted from t..:

Yusuke Miyazawa ; Narimasa Katsuta ; Tamaki Nara...
doi:10.1371/journal.pone.0296760.g003. , 2024

Link: https://doi.org/10.1371/journal.pone.0296760.g003

RT Journal
T1 <Title>Fifty 1-gram words with high features were extracted from the machine-learning model</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftunivoxfordfig:oai:figshare.com:article_25031078&Exemplar=1&LAN=DE
				
A1 Yusuke Miyazawa
A1 Narimasa Katsuta
A1 Tamaki Nara
A1 Shuko Nojiri
A1 Toshio Naito
A1 Makoto Hiki
A1 Masako Ichikawa
A1 Yoshihide Takeshita
A1 Tadafumi Kato
A1 Manabu Okumura
A1 Morikuni Tobita
YR 2024
K1 Medicine
K1 Neuroscience
K1 Biotechnology
K1 Science Policy
K1 Environmental Sciences not elsewhere classified
K1 Biological Sciences not elsewhere classified
K1 grouped similar words
K1 confusion assessment method
K1 abnormal patient behavior
K1 xlink "> covid
K1 associated delirium identified
K1 delirium associated
K1 temporal changes
K1 sudden change
K1 severe pneumonia
K1 results suggest
K1 respectively )
K1 related behaviors
K1 parameters may
K1 odds ratios
K1 odds ratio
K1 nursing records
K1 nervous system
K1 less sensitive
K1 hospital stay
K1 delirium prediction
K1 could predict
JF doi:10.1371/journal.pone.0296760.g003
LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0296760.g003
DO https://doi.org/10.1371/journal.pone.0296760.g003
SF ELIB - SuUB Bremen

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