Bone marrow transplants
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.
- Applying collaborative filtering techniques to data mining in bone marrow transplant records
- Knowledge-based isocenter selection in radiosurgery planning
- Guided undersampling classification for automated radiation therapy quality assurance of prostate cancer treatment
- Data mining in bone marrow transplant records to identify patients with high odds of survival
- Understanding machine learning classifier decisions in automated radiotherapy quality assurance (forthcoming)