Applications of Machine Learning for Improved Patient Selection and Therapy Recommendation

Monday, October 31, 2022 - 06:00 pm


Author : Brendan Odigwe

Advisor : Dr. Homayoun Valafar

Date : Oct 31, 2022

Time: 6:00 pm

Place : Meeting Link


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 come with some adverse side effects due to the nature of the medication. This would be particularly disappointing if the patients must be subjected to such medications without improving their health and quality of life. 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. Some life-saving therapies are cost intensive in addition to requiring risky, invasive 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. The datasets used in our work were varied in size as well as the hypothesis guiding our experiments, and as such, our approach to predictive analysis also varied. We have employed 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 have investigating multiple pressing healthcare problems, namely; Chronic Kidney Disease, Heart Failure, Sickle Cell Anemia, and Peripheral Arterial Disease. These approaches and others like it will positively influence medical decision-making, and administration of intervention procedures, and further the practice of precision medicine. The approaches and the rules generated produce a means of prioritizing patient data parameters and present us with the opportunity to extend medical practice and ultimately improve patient outcomes.