AI-based prediction tools for obesity in pregnancy

Rising maternal obesity, complexity, intervention rates and costs are challenging maternity care in Australia and globally. Our research has shown disparities in maternal care, with excess neonatal deaths across different models. We aim to advance implementation of a Maternity Learning Health System (LHS) across community priorities, research evidence, practice evidence and implementation to deliver evidence-based, value-based prevention and care to benefit mothers and babies nationally. Within the LHS, initial priorities include risk predication/ stratification to guide safe timing and mode of birth for women with obesity and implementing healthy lifestyle interventions to support women with obesity. We then aim to translate evidence into prevention through better care, better equity and better health for Australian mothers and babies. In this large program of work, we are seeking to recruit 4 talented PhD candidates. In this project, we seek a person with a strong biostatistics background, talented statistician or data analyst with health care knowledge. Objectives: Develop and validate innovative (AI-based) prediction tools to accurately identify maternal and neonatal risks associated with obesity in pregnancy, enabling personalised clinical decision-making. This PhD project will include: 1, A systematic review of obesity and its complications in pregnancy 2, A comparison of the predictive performance of existing risk prediction tools (which already incorporate BMI) with newly developed models specifically designed for pregnant women with obesity, focusing on pregnancy complications. 3,The development and inclusion of a shared decision-making aid.