Clinical Risk Prediction, AI/ML and Big Data for Diabetes in Pregnancy

Are you curious about using clinical risk prediction and big data to improve maternal health? Clinical risk prediction is a rapidly evolving field at the intersection of biostatistics, epidemiology, artificial intelligence/machine learning, biomarker science and large-scale linked health data. Gestational diabetes mellitus (GDM) impacts up to 1 in 5 Australian pregnancies, doubling over the past decade. Affected pregnancies eventually lead to half of affected women developing type 2 diabetes in the following decade. Preventative measures can reduce harmful outcomes however, women at high-risk need to be identified as realty as possible. This PhD opportunity focuses on developing and evaluating personalised prediction tools to support timely, targeted, equitable care for diabetes in pregnancy. Our team has previously developed and validated clinical risk prediction models across the pregnancy-postpartum continuum. Successful candidates will join a high-performing multidisciplinary team at the Monash Centre for Health Research and Implementation and gain advanced training in clinical prediction modelling, biostatistics, machine learning, data and biomarker analysis, health economics, implementation science and translation into practice. This MRFF-funded program focuses on developing and evaluating enhanced prediction tools to make predictions more precise, personalise and equitable by incorporating additional data sources, including novel biomarkers, large linked datasets and smarter screening approaches. Two PhD scholarships are available within this program: PhD 1 - Data and biomarker analysis for risk prediction Explore and analyse clinical, biomarker and linked health data to improve personalised risk prediction, using modern statistical and machine-learning methods across large datasets. PhD 2 - Risk prediction model updating, evaluation and translation Update and evaluate prediction tools and screening approaches, including their clinical, economic and equity value and pathways to implementation in practice, working with clinicians, researchers and consumers. Outputs will include high-impact methodological and applied publications, validated risk tools and a direct contribution to timely, targeted, equitable care in Australia. Scholarship and financial support This PhD position is supported by an Australian Government Research Training Program stipend at the current annual rate, with the support of a top-up scholarship available to domestic candidates for a period of 3 years only. Successful candidates will have the opportunity to complete additional paid research assistant hours, subject to performance and availability providing further income and related skill development. *This position offers conference funding of up to $2300 available to support presentations at national/international meetings. A background in clinical epidemiology, biostatistics, machine learning, public health, medicine, nutrition/dietetics or a related quantitative discipline is highly desirable. Applicants should have strong analytical skills and an interest in applying advanced quantitative methods to real-world clinical and public health problems.