Transplant matching and survival prediction
Bone marrow transplant survival prediction
Diseases requiring bone marrow transplants (BMTs), also called stem cell transplants, include leukemia, lymphoma, sickle cell disease and aplastic anemia, as well as some immunodeficiencies. As part of the conditioning process to prepare the patient for the bone marrow transplant, the patient may be treated with total body irradiation (TBI). The purpose of TBI is to eliminate the underlying disease and to suppress the recipient’s immune systems, thus preventing rejection of new donor stem cells. Once the conditioning treatment is complete, the patient receives the bone marrow transplant to restore healthy bone marrow function.
The success of a bone marrow transplant is uncertain, and depends on many factors, including underlying diagnosis, health status, donor relation, etc. Other important factors may exist that are not yet known or well-understood by clinicians. By examining historical records of BMTs, we develop machine learning tools to predict the success of a BMT with a particular patient and donor, using only data regularly collected during the course of treatment. We transform these predictions into conventional Kaplan-Meier survival functions to help clinicians and patients understand individualized survival probabilities and select the best course of treatment.
We particularly focus on single-center datasets to ensure that predictions are appropriate for the patient mix actually seen at the treating hospital, rather than use very large datasets covering many hospitals and regions, which may bias predictions with respect to any one hospital. Single-center datasets are small, requiring novel approaches to obtain satisfactorily accurate predictions.
Liver survival prediction
Similar to BMT survival prediction, we use machine learning to predict liver transplant survival. However, for liver transplants, we specifically focus on long-term survival of 20+ years. The ability to predict long-term survival can help clinicians and patients plan for possible re-transplants many years into the future, and may help better match available organs to patients on wait lists.
The focus on long-term survival creates prediction challenges, as only patients who were transplanted 20+ years ago have known long-term survival success, decreasing dataset size. Additionally, treatments and patients eligible for transplants have changed significantly in that time. We develop novel survival prediction techniques to predict the exact number of years of survival, and these methods can applied to any organ transplant scenario in which long-term survival is a significant factor in decision-making.
Kidney paired donation matching
Kidney paired donation programs allow patients registered with an incompatible donor to receive a suitable kidney from another donor, as long as the latter’s co-registered patient, if any, also receives a kidney from a different donor. The kidney exchange problem (KEP) aims to find an optimal collection of kidney exchanges taking the form of cycles and chains. KEP is complex optimization problem, which we solve using graph theory and decomposition methods.
- A branch-and-price algorithm enhanced by decision diagrams for the kidney exchange problem
- Machine learning for the prediction of survival post-allogeneic hematopoietic cell transplantation: A single-center experience
- A feasibility look to two-stage robust optimization in kidney exchange
- Deriving pandemic disease mitigation strategies by mining social contact networks
- Pandemic preparedness & response logistics: mining social contact networks for pandemic disease mitigation strategies