Enhancing the clinical relevance of haemorrhage prediction models in trauma[J]. MMR, 2024,11(3):467-468.
Cite this article as: Tandle S, Wohlgemut JM, Marsden MER, Pisirir E, Kyrimi E, Stoner RS, et al. Enhancing the clinical relevance of haemorrhage prediction models in trauma. Mil Med Res. 2023;10(1):43.
Enhancing the clinical relevance of haemorrhage prediction models in trauma[J]. MMR, 2024,11(3):467-468. DOI: 10.1186/s40779-023-00476-6.
Cite this article as: Tandle S, Wohlgemut JM, Marsden MER, Pisirir E, Kyrimi E, Stoner RS, et al. Enhancing the clinical relevance of haemorrhage prediction models in trauma. Mil Med Res. 2023;10(1):43. DOI: 10.1186/s40779-023-00476-6.
Enhancing the clinical relevance of haemorrhage prediction models in trauma
Peng HT , Siddiqui MM , Rhind SG , Zhang J , Teodoro da Luz L , Beckett A . Artificial intelligence and machine learning for hemorrhagic trauma care . Mil Med Res . 2023 ; 10 ( 1 ): 6 .
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Artificial intelligence and machine learning for hemorrhagic trauma care
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相关作者
Henry T. Peng
M. Musaab Siddiqui
Shawn G. Rhind
Jing Zhang
Luis Teodoro da Luz
Andrew Beckett
Yu Wang
Yong-Bo Xiang
相关机构
Defence Research and Development Canada, Toronto Research Centre
Sunnybrook Health Sciences Centre
. Michael’s Hospital
Royal Canadian Medical Services
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College