MIT EECS | Himawan Undergraduate Research and Innovation Scholar
Predicting and Understanding C.Difficile Risk Using Computational Models
- Artificial Intelligence and Machine Learning
John V. Guttag
In recent years, computer science has been used to model the spread and determine risk for various infectious diseases. This project aims to explore different computational models for C. Difficile , a bacterial infection that involves disruption of healthy bacteria in the colon and is often acquired in hospital settings. To investigate and combat hospital-acquired infections, our first aim is to provide daily risk predictions that patients in Massachusetts General Hospital (MGH) will acquire C. Difficile . Next, our research will investigate different antibiotics that have causal effects on increasing C. Difficile risk. Particularly, we are interested in determining which antibiotic out of a set of antibiotic options to treat a patient will pose the least causal risk of acquiring C. Difficile for the patient.
I am participating in the SuperUROP program because I want to gain more research experience in applying computer science to healthcare problems. I hope to explore different computational models and understand more about infectious diseases, especially in the context of the current pandemic. In addition, I would like to learn how to set up a successful long-term research project.