Antimicrobial Resistance (AMR) is the biggest cause of hospital infections that significantly impact patient survival, length of stay and health care costs estimated in the billions. Combined with electronic health data, genomic data can help researchers and clinicians discover early signs of antibiotics resistance or determine an individual’s risk of developing AMR. Genomics can point to the underlying causes of clinical changes, leading to more personalized, effective treatments. This project focuses on utilizing machine learning applications to process the textual data from to study host-pathogen covariates in an infection state.
Computational biology, genomics, data modelling, ML, NLP
Masters by research
Top-up scholarship funding available
Alfred Centre, The Alfred Hospital