Abstract
Donor selection for Hematopoietic Stem Cell Transplant often requires physicians to manually select 3 to 5 donors from a list of 100s of genetically compatible donors as identified by HLA-based matching algorithms. The decision process is complicated by a lack of strict guidelines governing a "secondary" selection process, which is based upon non-HLA donor attributes. Our research is aimed at modeling this "secondary" decision process which can help physicians choose the right donors, based on donor attributes and historical choice behavior. Proposed black box models will help in improving selection consistency.
Original language | English (US) |
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Title of host publication | Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 831-836 |
Number of pages | 6 |
ISBN (Electronic) | 9781509002870 |
DOIs | |
State | Published - Mar 2 2016 |
Event | IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States Duration: Dec 9 2015 → Dec 11 2015 |
Publication series
Name | Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 |
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Other
Other | IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 |
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Country/Territory | United States |
City | Miami |
Period | 12/9/15 → 12/11/15 |
Bibliographical note
Funding Information:This work was supported, in part, by the Faculty in Industry grant from the University of Minnesota Informatics Institute (UMII), and by ONR grant N00014-14-1-0848
Publisher Copyright:
© 2015 IEEE.
Keywords
- Donor selection
- Predictive data analytics
- SVM classification
- Stem cell transplant
- Unbalanced data