Predicting the Availability of Hematopoietic Stem Cell Donors Using Machine Learning
Autor: | David Winstone, Guy Parkes, Leszek Gasieniec, Hong Lin, Andrew Hadley, Ying Li, Rachel Pawson, Ausra Masiliune, Prudence W. H. Wong |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
medicine.medical_treatment
Hematopoietic stem cell transplantation Logistic regression Machine learning computer.software_genre Machine Learning 03 medical and health sciences 0302 clinical medicine medicine Humans Transplantation Homologous Retrospective Studies Transplantation Receiver operating characteristic business.industry Donor selection Hematopoietic Stem Cell Transplantation Hematopoietic stem cell Hematology Hematopoietic Stem Cells Tissue Donors Support vector machine medicine.anatomical_structure 030220 oncology & carcinogenesis Cohort Alternating decision tree Artificial intelligence business computer 030215 immunology |
Zdroj: | Biology of Blood and Marrow Transplantation |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |