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Machine learning for biomedicine

Computational biology

Areas:

  • Genomics
  • Drug design
  • Data efficiency

Healthcare

Modern hospitals and medical centres have collected huge amount of clinical data for hundreds of millions of patients over the past decades. However, how to make the best out of the data for improving clinincal services remains the major question. This research aims at characterising the data using statistical models and applying the state-of-the-art machine learning techniques for representation, clustering and prediction both at the patient and the cohort levels.

Areas:

  • Intensive care units (ICU)
  • Mental health
  • Preterm birth
  • Population health
  • Electronic medical records
  • EEG
  • Medical imaging
  • Patient flow



Preprints

  1. An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments, T Nguyen, T Tran, S Gopakumar, D Phung, S Venkatesh, arXiv preprint arXiv:1605.01116

Publications

2017

2016

2015

2014

2013

2004-2010

  • Boosted Markov networks for activity recognition, Truyen Tran, Hung Hai Bui and Svetha Venkatesh, In Proc. of International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP2005), 5-8 Dec, Melbourne, Australia.

 Patents