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Interpretable natural language processing of biomedical and healthcare data

Description 
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.
Essential criteria: 
Minimum entry requirements can be found here: https://www.monash.edu/admissions/entry-requirements/minimum
Keywords 
Computational biology, genomics, data modelling, ML, NLP
Available options 
PhD/Doctorate
Masters by research
Honours
BMedSc(Hons)
Time commitment 
Full-time
Part-time
Top-up scholarship funding available 
No
Physical location 
Alfred Centre, The Alfred Hospital
Co-supervisors 
Dr 
Sonika Tyagi

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