Date of Award
Open Access Dissertation
Computer Science and Engineering
The public health domain continues to battle with illness and the growing need for continuous advancement in our approach to clinical care. Individuals experiencing certain conditions undergo tried and tested therapies and medications, practices that have become the mainstay and standard of care in clinical medicine. As with all therapies and medications, they don't always work the same way and do not work for everyone. Some Treatment regimens, like Hydroxyurea medication, which is commonly administered to Sickle cell anemia patients, come with some adverse side effects due to the chemotherapeutic nature of the drug. This would be particularly disappointing if the patients must be subjected to such medications without improving their health and quality of life. Some patients, like those battling chronic kidney disease face a more arduous healthcare journey due to the degenerative nature of their illness, coupled with the fact that there are limited tools to forecast the rate of progression of the disease. Asides from the physical toll patients could be subjected to; there is the matter of the economic impact of these therapies on the patients, their family members, insurance companies and even the government. Life-saving therapies like cardiac re-synchronization therapy are cost intensive in addition to requiring surgical procedures. It would be great if we had more ways of identifying patients that are most likely to receive significant benefits from recommended therapies before they are subjected to them.
We will employ a series of machine learning techniques to create models that can indicate a patient's response pattern to recommended therapy. To ensure that our approaches are widely applicable, we would be investigating multiple pressing healthcare problems, namely; Chronic Kidney Disease, Heart Failure, Sickle Cell Anemia, and Peripheral Arterial Disease.
Odigwe, B. E.(2022). Applications of Machine Learning for Improved Patient Selection and Therapy Recommendations. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/7062