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Title: Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation
Authors: Arai, Yasuyuki  kyouindb  KAKEN_id  orcid (unconfirmed)
Kondo, Tadakazu  kyouindb  KAKEN_id  orcid (unconfirmed)
Fuse, Kyoko
Shibasaki, Yasuhiko
Masuko, Masayoshi
Sugita, Junichi
Teshima, Takanori
Uchida, Naoyuki
Fukuda, Takahiro
Kakihana, Kazuhiko
Ozawa, Yukiyasu
Eto, Tetsuya
Tanaka, Masatsugu
Ikegame, Kazuhiro
Mori, Takehiko
Iwato, Koji
Ichinohe, Tatsuo
Kanda, Yoshinobu
Atsuta, Yoshiko
Author's alias: 新井, 康之
近藤, 忠一
Issue Date: 26-Nov-2019
Publisher: American Society of Hematology
Journal title: Blood Advances
Volume: 3
Issue: 22
Start page: 3626
End page: 3634
Abstract: Acute graft-versus-host disease (aGVHD) is 1 of the critical complications that often occurs following allogeneic hematopoietic stem cell transplantation (HSCT). Thus far, various types of prediction scores have been created using statistical calculations. The primary objective of this study was to establish and validate the machine learning–dependent index for predicting aGVHD. This was a retrospective cohort study that involved analyzing databases of adult HSCT patients in Japan. The alternating decision tree (ADTree) machine learning algorithm was applied to develop models using the training cohort (70%). The ADTree algorithm was confirmed using the hazard model on data from the validation cohort (30%). Data from 26 695 HSCT patients transplanted from allogeneic donors between 1992 and 2016 were included in this study. The cumulative incidence of aGVHD was 42.8%. Of >40 variables considered, 15 were adapted into a model for aGVHD prediction. The model was tested in the validation cohort, and the incidence of aGVHD was clearly stratified according to the categorized ADTree scores; the cumulative incidence of aGVHD was 29.0% for low risk and 58.7% for high risk (hazard ratio, 2.57). Predicting scores for aGVHD also demonstrated the link between the risk of development aGVHD and overall survival after HSCT. The machine learning algorithms produced clinically reasonable and robust risk stratification scores. The relatively high reproducibility and low impacts from the interactions among the variables indicate that the ADTree algorithm, along with the other data-mining approaches, may provide tools for establishing risk score.
Rights: This research was originally published in Blood Advances. Arai Y. et. al.; Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation. Blood Adv 2019; 3 (22): 3626–3634. © the American Society of Hematology.
DOI(Published Version): 10.1182/bloodadvances.2019000934
PubMed ID: 31751471
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