Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis. - Kushan De Silva; Wai Kit Lee; Andrew Forbes; Ryan T. Demmer; Christopher Barton; Joanne Enticott (Int. J. Medical Informatics, 2020)

Clinical notes as prognostic markers of mortality associated with diabetes mellitus following critical care: A retrospective cohort analysis using machine learning and unstructured big data. - Kushan De Silva; Noel Mathews; Helena Teede; Andrew Forbes; Daniel Jönsson; Ryan T. Demmer; Joanne Enticott (Comput. Biol. Medicine, 2021)

Use of My Health Record by Clinicians in the Emergency Department: An Analysis of Log Data. - Alexandra K. Mullins; Heather Morris; Joanne Enticott; Michael Ben-Meir; David Rankin; Kumar Mantripragada; Helen Skouteris (Frontiers Digit. Health, 2021)

A Log Analysis Exploring the Predictors of Electronic Health Record Access by Clinicians for Consumers Aged ≥65 Who Present to the Emergency Department. - Alexandra K. Mullins; Helen Skouteris; Heather Morris; Joanne Enticott (MIE, 2022)

Predictors of clinician use of Australia's national health information exchange in the emergency Department: An analysis of log data. - Alexandra K. Mullins; Helen Skouteris; David Rankin; Heather Morris; Kostas Hatzikiriakidis; Joanne Enticott (Int. J. Medical Informatics, 2022)

Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model. - Yitayeh Belsti; Lisa Moran; Lan Du 002; Aya Mousa; Kushan De Silva; Joanne Enticott; Helena Teede (Int. J. Medical Informatics, 2023)

External validation of risk prediction model for gestational diabetes: Individual participant data meta-analysis of randomized trials. - Sanjeeva Ranasinha; Joanne Enticott; Cheryce L. Harrison; Shakila Thangaratinam; Rui Wang; Helena J. Teede (Int. J. Medical Informatics, 2024)

Prediction of pre-eclampsia with machine learning approaches: Leveraging important information from routinely collected data. - Sofonyas Abebaw Tiruneh; Daniel Lorber Rolnik; Helena J. Teede; Joanne Enticott (Int. J. Medical Informatics, 2024)

Multi-Label Bayesian Active Learning with Inter-Label Relationships. - Yuanyuan Qi; Jueqing Lu; Xiaohao Yang; Joanne Enticott; Lan Du 002 (UAI, 2025)

Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study. - Sofonyas Abebaw Tiruneh; Daniel Lorber Rolnik; Helena Teede; Joanne Enticott (Comput. Biol. Medicine, 2025)