Machine learning-based scoring models to predict hematopoietic stem cell mobilization in allogeneic donors

Authors

Jingyu Xiang, Washington University School of Medicine in St. Louis
Min Shi, Washington University School of Medicine in St. Louis
Mark A Fiala, Washington University School of Medicine in St. Louis
Feng Gao, Washington University School of Medicine in St. Louis
Michael P Rettig, Washington University School of Medicine in St. Louis
Geoffrey L Uy, Washington University School of Medicine in St. Louis
Mark A Schroeder, Washington University School of Medicine in St. Louis
Katherine N Weilbaecher, Washington University School of Medicine in St. Louis
Keith E Stockerl-Goldstein, Washington University School of Medicine in St. Louis
Shamim Mollah, Washington University School of Medicine in St. Louis
John F DiPersio, Washington University School of Medicine in St. Louis

Journal

Blood Advances

Publication Date

4-12-2022

Volume

6

Issue

7

First Page

1991

Last Page

2000

Document Type

Open Access Publication

DOI

10.1182/bloodadvances.2021005149

Rights and Permissions

Xiang J, Shi M, Fiala MA, Gao F, Rettig MP, Uy GL, Schroeder MA, Weilbaecher KN, Stockerl-Goldstein KE, Mollah S, DiPersio JF. Machine learning-based scoring models to predict hematopoietic stem cell mobilization in allogeneic donors. Blood Adv. 2022 Apr 12;6(7):1991-2000. doi.org/10.1182/bloodadvances.2021005149 Copyright 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights

Recommended Citation

Xiang, Jingyu; Shi, Min; Fiala, Mark A; Gao, Feng; Rettig, Michael P; Uy, Geoffrey L; Schroeder, Mark A; Weilbaecher, Katherine N; Stockerl-Goldstein, Keith E; Mollah, Shamim; and DiPersio, John F, "Machine learning-based scoring models to predict hematopoietic stem cell mobilization in allogeneic donors." Blood Advances. 6, 7. 1991 - 2000. (2022).
https://digitalcommons.wustl.edu/open_access_pubs/11793