BIO

I am an International Honor Computer Science major student at FSU, and expect graduation year 2024 for Bachelor of Science degree. I’m really looking forward to learn, study and even research on topics that relate to Computer Science.

Machine Learning in predicting outcomes of organ transplantation

Abstract

This poster is an examination of machine learning in predicting the outcome of organ transplantations, specifically liver, and heart. Due to the already large number of potential variables that have to be considered for predicting, machine learning can be most efficiently performed these tasks. Thus, various Machine Learning models such as Random Forest and XGBoost have been tested, even Deep Learning models are being used to research on. The data that use to train these models are also varied such as Pediatric Heart Transplant Society database, UNOS registry database and data from a large pediatric organ transplant center. This indicates that the machine learning models are flexible enough to be able to train and use for different purposes not just predicting the outcomes of organ transplant. Regarding to Deep Learning models, results from researches showed that till now it has not yield superior result compare to traditional Machine Learning models. After training the models from the large existing medical data, despite the overall prediction performance can be limited in certain aspects, the Machine Learning model along with Deep Learning model which has proven to have potential to assist and provide crucial informations about potential post-transplant outcomes for patients so physicians, transplantation teams can make better decisions to yield the best possible outcomes for the patients.

Keywords: Machine Learning, Post Organ Transplant, Health