Unrelated Donor Selection for Stem Cell Transplants using Predictive Modelling

Unrelated Donor selection for a Hematopoietic Stem Cell Transplant is a complex multi-stage process. Choosing the most suitable donor from a list of Human Leukocyte Antigen (HLA) matched donors can be challenging to even the most experienced physicians and search coordinators. The process involves experts sifting through potentially thousands of genetically compatible donors based on multiple factors. We propose a Machine Learning approach to donor selection based on historical searches performed and selections made for these searches. We describe the process of building a computational model to mimic the donor selection decision process and show benefits of using the proposed model in this study.

• Publication year

  2018

• Venue

  bioRxiv

• Publication date

  2018-01-04

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1101/242735
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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