Predicting short- and long-term outcomes of pregnancy to optimise maternal health care (Honours & Master)

Primary supervisor

Lan Du

Co-supervisors

  • Emily Callander

As a pregnancy approaches term (the point at which the foetus is considered fully developed), decisions are made about the timing of birth and the way babies are born. These decisions are incredibly challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up opportunities for a dynamic, ‘Learning Health System’ – where data can be harnessed to inform real-time and personalised decision-making. Existing linked administrative databases already capture Australian women and children's observed birth events and their actual health and well-being outcomes. The latest machine learning and artificial intelligence advancements can mine these datasets to create prediction models that can forecast the likely outcomes of current practices.

Student cohort

Double Semester

Aim/outline

  • build a casual map based on the large database of maternal care with a human-in-loop approach;
  • apply cutting-edge machine learning and risk prediction statistical techniques to predicting each short-term, long-term and cost outcomes.
  • explore the use of near-time data from health care providers to update these models over time.

 

Required knowledge

  • the candidate should have basic knowledge about machine/deep learning, e.g., various classification/regression methods.
  • the candidate should have good python/R programming skill.

The candidate will work closely with experts in both machine learning and clinicians.