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Table_2_Machine-Learning Approaches for Predicting the Need..:
Syunsuke Yamanaka
;
Koji Morikawa
;
Hiroyuki Azuma
...
doi:10.3389/fmed.2022.846525.s003. , 2022
Link:
https://doi.org/10.3389/fmed.2022.846525.s003
RT Journal T1
Table_2_Machine-Learning Approaches for Predicting the Need of Oxygen Therapy in Early-Stage COVID-19 in Japan: Multicenter Retrospective Observational Study.DOCX
UL https://suche.suub.uni-bremen.de/peid=base-ftfrontimediafig:oai:figshare.com:article_19217997&Exemplar=1&LAN=DE A1 Syunsuke Yamanaka A1 Koji Morikawa A1 Hiroyuki Azuma A1 Maki Yamanaka A1 Yoshimitsu Shimada A1 Toru Wada A1 Hideyuki Matano A1 Naoki Yamada A1 Osamu Yamamura A1 Hiroyuki Hayashi YR 2022 K1 Dermatology K1 Emergency Medicine K1 Gastroenterology and Hepatology K1 Geriatrics and Gerontology K1 Intensive Care K1 Medical Genetics (excl. Cancer Genetics) K1 Nephrology and Urology K1 Nuclear Medicine K1 Orthopaedics K1 Otorhinolaryngology K1 Pathology (excl. Oral Pathology) K1 Radiology and Organ Imaging K1 Foetal Development and Medicine K1 Obstetrics and Gynaecology K1 Family Care K1 Primary Health Care K1 Medical and Health Sciences not elsewhere classified K1 COVID-19 K1 machine learning K1 prognostic model K1 medical triage K1 multicenter K1 PROBAST K1 TRIPOD JF doi:10.3389/fmed.2022.846525.s003 LK http://dx.doi.org/https://doi.org/10.3389/fmed.2022.846525.s003 DO https://doi.org/10.3389/fmed.2022.846525.s003 SF ELIB - SuUB Bremen
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