TY - JOUR
T1 - Predicting the Availability of Hematopoietic Stem Cell Donors Using Machine Learning
AU - Li, Ying
AU - Masiliune, Ausra
AU - Winstone, David
AU - Gasieniec, Leszek
AU - Wong, Prudence
AU - Lin, Hong
AU - Pawson, Rachel
AU - Parkes, Guy
AU - Hadley, Andrew
N1 - Funding Information:
Financial disclosure: This work was funded by the British Bone Marrow Donor Appeal (BBMDA).
Funding Information:
The authors thank the University of Liverpool for their helpful collaboration on machine learning knowledge transfer. Financial disclosure: This work was funded by the British Bone Marrow Donor Appeal (BBMDA). Conflict of interest statement: There are no conflicts of interest to report. Authorship statement: Y.L. conceived and designed the study. Y.L. and A.M. developed and validated the machine learning models, under the supervision of L.G. and P.W. and with clinical input from R.P. A.M. D.W. and H.L. collected the data and ensured quality of the data given to the analysis. Y.L. G.P. and A.H. applied and secured the funding of this project. Y.L. wrote the first draft of the article, which was critically revised and approved by all authors.
Publisher Copyright:
© 2020 American Society for Transplantation and Cellular Therapy
PY - 2020/8
Y1 - 2020/8
N2 - Hematopoietic stem cell transplantation (HSCT) is firmly established as an important curative therapy for patients with hematologic malignancies and other blood disorders. Apart from finding HLA-matched donors during the HSCT process, donor availability remains a key consideration as the time taken from diagnosis to transplant is recognized to adversely affect patient outcome. In this study, we aimed to develop and validate a machine learning approach to predict the availability of stem cell donors. We retrospectively collected a data set containing 10,258 verification typing requests made during the HSCT process in the British Bone Marrow Registry (BBMR) between January 1, 2013, and December 31, 2018. Three machine learning algorithms were implemented and compared, including boosted decision trees (BDTs), logistic regression, and support vector machines. Area under the receiver operating characteristic curve (AUC) was primarily used to assess the algorithms. The experimental results showed that BDTs performed better in predicting the availability of BBMR donors. The overall predictive power of the model, using AUC on the test cohort of 2052 records, was found to be 0.826. Our findings show that machine learning can predict the availability of donors with a high degree of accuracy. We propose the use of the BDT machine learning approach to predict the availability of BBMR donors and use the predictive scores during the HSCT process to ensure patients with blood cancers or disorders receive a transplant at the optimum time.
AB - Hematopoietic stem cell transplantation (HSCT) is firmly established as an important curative therapy for patients with hematologic malignancies and other blood disorders. Apart from finding HLA-matched donors during the HSCT process, donor availability remains a key consideration as the time taken from diagnosis to transplant is recognized to adversely affect patient outcome. In this study, we aimed to develop and validate a machine learning approach to predict the availability of stem cell donors. We retrospectively collected a data set containing 10,258 verification typing requests made during the HSCT process in the British Bone Marrow Registry (BBMR) between January 1, 2013, and December 31, 2018. Three machine learning algorithms were implemented and compared, including boosted decision trees (BDTs), logistic regression, and support vector machines. Area under the receiver operating characteristic curve (AUC) was primarily used to assess the algorithms. The experimental results showed that BDTs performed better in predicting the availability of BBMR donors. The overall predictive power of the model, using AUC on the test cohort of 2052 records, was found to be 0.826. Our findings show that machine learning can predict the availability of donors with a high degree of accuracy. We propose the use of the BDT machine learning approach to predict the availability of BBMR donors and use the predictive scores during the HSCT process to ensure patients with blood cancers or disorders receive a transplant at the optimum time.
KW - allogeneic hematopoietic stem cell transplantation
KW - donor availability
KW - donor selection
KW - machine learning
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U2 - 10.1016/j.bbmt.2020.03.026
DO - 10.1016/j.bbmt.2020.03.026
M3 - Article
C2 - 32413415
AN - SCOPUS:85085765412
SN - 1083-8791
VL - 26
SP - 1406
EP - 1413
JO - Biology of Blood and Marrow Transplantation
JF - Biology of Blood and Marrow Transplantation
IS - 8
ER -