Computable evidence lab

The Centre for Health Informatics' Computable Evidence Lab (CEL) members research how clinical decisions can be made quickly and safely based on evidence. We focus on ways in which automation can help gather, synthesize and disseminate evidence to inform decision making at the right place and time for the decision.

There are three main themes across the lab:

1.      Machine Learning - algorithms that find patterns in data for a variety of applications such as prediction, classification and artificial intelligence.

2.      Natural Language Processing - algorithms that find, appraise and extract information from text.

3.      Heuristic Systems - rule based systems that encapsulate and use domain expertise to solve a particular problem.

Evidence based medicine already provides a robust model for gathering, synthesis and dissemination of evidence in the systematic review model. At CEL we follow this model for different kinds of evidence summaries and different kinds of evidence from sources such as genomics and electronic health records. The Computable Evidence Lab is leading the way for new ways to think about evidence. Evidence should be ubiquitous, inexpensive and available to enable safe and effective healthcare systems.

For more information or to join our team

Contact Dr Guy Tsafnat, guy.tasfnat@mq.edu.au+61 2 9850 2430

Stream Team Members