Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation-Reference-Cited by-同舟云学术

Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation

Published:2019-11-21 Issue:22 Volume:3 Page:3626-3634
ISSN:2473-9529
Container-title:Blood Advances
language:en
Short-container-title:

Author:

Arai Yasuyuki¹²^ORCID,Kondo Tadakazu²^ORCID,Fuse Kyoko³^ORCID,Shibasaki Yasuhiko³,Masuko Masayoshi³,Sugita Junichi⁴^ORCID,Teshima Takanori⁴^ORCID,Uchida Naoyuki⁵,Fukuda Takahiro⁶,Kakihana Kazuhiko⁷^ORCID,Ozawa Yukiyasu⁸,Eto Tetsuya⁹,Tanaka Masatsugu¹⁰,Ikegame Kazuhiro¹¹,Mori Takehiko¹²,Iwato Koji¹³,Ichinohe Tatsuo¹⁴,Kanda Yoshinobu¹⁵,Atsuta Yoshiko¹⁶¹⁷

Affiliation:

1. Department of Transfusion Medicine and Cell Therapy and

2. Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan;

3. Department of Hematology, Endocrinology and Metabolism, Niigata University Faculty of Medicine, Niigata, Japan;

4. Department of Hematology, Hokkaido University Hospital, Hokkaido, Japan;

5. Department of Hematology, Federation of National Public Service Personnel Mutual Aid Associations, Toranomon Hospital, Tokyo, Japan;

6. Department of Hematopoietic Stem Cell Transplantation, National Cancer Center Hospital, Tokyo, Japan;

7. Hematology Division, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, Tokyo, Japan;

8. Department of Hematology, Japanese Red Cross Nagoya First Hospital, Aichi, Japan;

9. Department of Hematology, Hamanomachi Hospital, Fukuoka, Japan;

10. Department of Hematology, Kanagawa Cancer Center, Kanagawa, Japan;

11. Division of Hematology, Department of Internal Medicine, Hyogo College of Medicine, Hyogo, Japan;

12. Division of Hematology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan;

13. Department of Hematology, Hiroshima Red Cross Hospital & Atomic-bomb Survivors Hospital, Hiroshima, Japan;

14. Department of Hematology and Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima, Japan;

15. Division of Hematology, Jichi Medical University, Saitama, Japan;

16. Japanese Data Center for Hematopoietic Cell Transplantation, Nagoya, Japan; and

17. Department of Healthcare Administration, Nagoya University Graduate School of Medicine, Nagoya, Japan

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.

Publisher

American Society of Hematology

Subject

Hematology

Link

http://ashpublications.org/bloodadvances/article-pdf/3/22/3626/1542488/advancesadv2019000934.pdf

Reference31 articles.

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2. Risk factors and prognosis of hepatic acute GvHD after allogeneic hematopoietic cell transplantation;Arai;Bone Marrow Transplant,2016

3. A prognostic score for acute graft-versus-host disease based on biomarkers: a multicentre study;Levine;Lancet Haematol,2015

4. A refined risk score for acute graft-versus-host disease that predicts response to initial therapy, survival, and transplant-related mortality;MacMillan;Biol Blood Marrow Transplant,2015

5. Big data analytics and machine learning: 2015 and beyond;Passos;Lancet Psychiatry,2016

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