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  • [\'School of Electronics Engineering and Computer Science, Peking University, Beijing, People\'s Republic of China.\', \'Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, People\'s Republic of China.\', \'National Institute of Health Data Science, Peking University, Beijing, People\'s Republic of China.\', \'Institute of Medical Technology, Health Science Center of Peking University, Beijing, People\'s Republic of China.\', \'School of Electronics Engineering and Computer Science, Peking University, Beijing, People\'s Republic of China. leehy@pku.edu.cn.\', \'Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, People\'s Republic of China. leehy@pku.edu.cn.\', \'Institute of Population Research, Peking University, No.5 Yiheyuan Road, Beijing, 100871, People\'s Republic of China. zhenjie.wang@pku.edu.cn.\']
?:citedBy
  • -1
?:creator
?:doi
?:doi
  • 10.1186/s12911-020-01359-9
?:hasPublicationType
?:journal
  • BMC medical informatics and decision making
is ?:pmid of
?:pmid
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  • 33557818
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?:rankingScore_SJR
  • 0.812
?:rankingScore_hIndex
  • 56
?:title
  • Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning.
?:type
?:year
  • 2021

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