MIT EECS — Mason Undergraduate Research and Innovation Scholar
Building a More Personalized Colon Cancer Prognosis Tool using the Partners Healthcare Clinical Data Set
Our goal is to build a more personalized web-based tool to help patients with colon cancer understand the potential prognosis they might encounter as a result of the disease. We hope that the tool, using additional features gleaned from ancillary EMR data, will provide fine-tuned forecasts for colon cancer stage advancement, mortality risk, and potential interventions or treatments for the patient. We hope such information can better influence the personalized management of colon cancer and better inform patients about their current health state after they are diagnosed with colon cancer.
After taking 6.034 and a machine learning summer internship, I became interested in learning about out how medical decisions could be modeled as machine learning problems that draw on existing medical data to assist in clinical decision making. I’m excited to be working on a SuperUROP that will help me gain a better understanding of both the medical field and applying AI in real-world problems.