Published in
American Society of Hematology, Blood Advances, 22(3), p. 3626-3634, 2019
DOI: 10.1182/bloodadvances.2019000934
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Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation
Journal article published in 2019 by
Yasuyuki Arai 
,
Tadakazu Kondo ,
Kyoko Fuse ,
Yasuhiko Shibasaki,
Masayoshi Masuko,
Junichi Sugita ,
Takanori Teshima ,
Naoyuki Uchida,
Takahiro Fukuda,
Kazuhiko Kakihana ,
Yukiyasu Ozawa,
Tetsuya Eto,
Masatsugu Tanaka,
Kazuhiro Ikegame,
Takehiko Mori
and other authors.
,
Tadakazu Kondo ,
Kyoko Fuse ,
Yasuhiko Shibasaki,
Masayoshi Masuko,
Junichi Sugita ,
Takanori Teshima ,
Naoyuki Uchida,
Takahiro Fukuda,
Kazuhiko Kakihana ,
Yukiyasu Ozawa,
Tetsuya Eto,
Masatsugu Tanaka,
Kazuhiro Ikegame,
Takehiko Mori
and other authors.
This paper is made freely available by the publisher.
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Abstract
Key Points The machine learning algorithms produced clinically reasonable and robust risk stratification scores for aGVHD. Predicting scores for aGVHD also demonstrated the link between risk of development of aGVHD and overall survival after HSCT.