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Improving Workflow of Call-Takers for Recognizing Cardiac Arrest from Triple-Zero Calls

Primary supervisor

Lizhen Qu


  • Prof. Karen Smith
  • Dr. Resmi Nair

This project is within the scope of the project “Artificial Intelligence in carDiac arrEst” (AIDE), which was led by Ambulance Victoria (AV) in Australia, involving a team of researchers at Monash University. This AIDE project has developed an Artificial Intelligence (AI) tool to recognise potential Out-of-Hospital-Cardiac Arrest (OHCA) during the Triple Zero (000) call by using transcripts produced by Microsoft Automatic Speech Recognition service. In the next step, we aim to optimise the workflow of call-takers and investigate which workflows can lead to earlier identification of OHCA. This is still an open challenge of call centre triage and requires counterfactual analysis of call histories. In this project, the student will develop the counterfactual analysis tool to find out if OHCA can be identified earlier if call-takers had asked different questions. 


Student cohort

Single Semester
Double Semester


The goal of the thesis is to develop a novel causal analysis tool to examine call histories for improving workflow of call-takers. You are also expected to conduct extensive experiments to evaluate if the workflows suggested by the tool indeed improve productivity of call-takers on real-world triple-zero calls.

Required knowledge

Students should have excellent grades in machine learning and relevant math courses. Strong programming skills are essential. Preference will be given to students who have previous practical experiences on machine learning tools such as Tensorflow, Pytorch, and ScikitLearn. Students with an interest in pursuing PhD research or careers in research are especially encouraged to apply.