Researcher

Mr Oisin Brian Fitzgerald

Biography

Oisin is an early career researcher and Senior Data Scientist in the National Perinatal Epidemiology and Statistics Unit. He completed his PhD in health data science at the Centre for Big Data Research in Health (CBDRH) at UNSW Sydney in collaboration with eHealth NSW and the CSIRO in 2023. He has published research on topics including novel deep learning methods for forecasting of physiological time series, causal inference, risk prediction...view more

Oisin is an early career researcher and Senior Data Scientist in the National Perinatal Epidemiology and Statistics Unit. He completed his PhD in health data science at the Centre for Big Data Research in Health (CBDRH) at UNSW Sydney in collaboration with eHealth NSW and the CSIRO in 2023. He has published research on topics including novel deep learning methods for forecasting of physiological time series, causal inference, risk prediction and the impact of hyperglycaemia in critically ill patients. He currently primarily works on research related to assisted reproductive technology, an area in which he has authored several public and government reports, contributed to the development of clinical registries, developed statistical methodologies and software for ART clinic quality control, and more recently worked on the development of the YourIVFSuccess website which won the Research Australia "Innovative Use of Data" award in 2023. As part of the YourIVFSuccess website he lead the development of the Estimator, a machine learning tool built using >500,000 ART cycles that informs users of their chance of a livebirth from ART, and he continues to lead research  investigating potential future improvements.


My Qualifications

PhD, UNSW Sydney (2023)

Master of Statistics, UNSW Sydney (2018)


My Research Activities

  • Development of prediction models for success from IVF treatment
  • Investigation of the relative importance of patient and treatment factors in the prediction of success from IVF treatment
  • Comparison of IVF treatment options (e.g. traditional IVF vs. ICSI) on patient outcomes
  • Interpretable machine learning methods
  • Transportability of clinical prediction models across populations and time

 

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