Associate Professor Sebastiano Barbieri
Associate Professor

Sebastiano Barbieri

Email: 

Background

Dr. Sebastiano Barbieri is Associate Professor and Principal Research Fellow at the Queensland Digital Health Centre, University of Queensland (UQ) and Adjunct Associate Professor at the Centre for Big Data Research in Health, University of New South Wales (UNSW). His work lies at the intersection of machine learning and healthcare, where he develops innovative computational methods to tackle pressing challenges in medicine.

Aiming to improve patient outcomes and streamline clinical workflows, Dr. Barbieri develops machine learning models tailored to real-world healthcare applications. His current research spans risk prediction using electronic medical records, medical image processing, and the safe and effective integration of AI into clinical decision-making processes.

A strong advocate for responsible AI in healthcare, Dr. Barbieri champions the use of emerging technologies such as synthetic data generation and federated learning. These approaches not only enhance data accessibility and privacy but also accelerate the development of robust, data-driven solutions for digital health.

Availability

Associate Professor Sebastiano Barbieri is:
Available for supervision

Qualifications

  • Bachelor of Mathematics, Universität des Saarlandes
  • Masters (Coursework) of Image Processing, Universität des Saarlandes
  • Doctor of Philosophy of Computer Science, Jacobs University
  • Masters (Coursework) of Biostatistics, Macquarie University

Search Professor Sebastiano Barbieri’s works on UQ eSpace

33 works between 2017 and 2026
All (33) Journal Article (31) Conference Publication (2)

21 - 33 of 33 works

2022

Journal Article

Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach

Barbieri, Sebastiano, Mehta, Suneela, Wu, Billy, Bharat, Chrianna, Poppe, Katrina, Jorm, Louisa and Jackson, Rod (2022). Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach. International Journal of Epidemiology, 51 (3), 931-944. doi: 10.1093/ije/dyab258

Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach

2022

Journal Article

A machine learning approach to predict the added-sugar content of packaged foods

Davies, Tazman, Louie, Jimmy Chun Yu, Ndanuko, Rhoda, Barbieri, Sebastiano, Perez-Concha, Oscar and Wu, Jason H. Y (2022). A machine learning approach to predict the added-sugar content of packaged foods. Journal of Nutrition, 152 (1), 343-349. doi: 10.1093/jn/nxab341

A machine learning approach to predict the added-sugar content of packaged foods

2021

Journal Article

Using administrative data to predict cessation risk and identify novel predictors among new entrants to opioid agonist treatment

Bharat, Chrianna, Degenhardt, Louisa, Dobbins, Timothy, Larney, Sarah, Farrell, Michael and Barbieri, Sebastiano (2021). Using administrative data to predict cessation risk and identify novel predictors among new entrants to opioid agonist treatment. Drug and Alcohol Dependence, 228 109091, 1-8. doi: 10.1016/j.drugalcdep.2021.109091

Using administrative data to predict cessation risk and identify novel predictors among new entrants to opioid agonist treatment

2021

Journal Article

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

Kaandorp, Misha P. T., Barbieri, Sebastiano, Klaassen, Remy, van Laarhoven, Hanneke W. M., Crezee, Hans, While, Peter T., Nederveen, Aart J. and Gurney-Champion, Oliver J. (2021). Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients. Magnetic Resonance in Medicine, 86 (4), 2250-2265. doi: 10.1002/mrm.28852

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

2021

Journal Article

Psychotropic medicine prescribing and polypharmacy for people with dementia entering residential aged care: the influence of changing general practitioners

Welberry, Heidi J, Jorm, Louisa R, Schaffer, Andrea L, Barbieri, Sebastiano, Hsu, Benjumin, Harris, Mark F, Hall, John and Brodaty, Henry (2021). Psychotropic medicine prescribing and polypharmacy for people with dementia entering residential aged care: the influence of changing general practitioners. Medical Journal of Australia, 215 (3), 130-136. doi: 10.5694/mja2.51153

Psychotropic medicine prescribing and polypharmacy for people with dementia entering residential aged care: the influence of changing general practitioners

2021

Journal Article

Big data and predictive modelling for the opioid crisis: existing research and future potential

Bharat, Chrianna, Hickman, Matthew, Barbieri, Sebastiano and Degenhardt, Louisa (2021). Big data and predictive modelling for the opioid crisis: existing research and future potential. The Lancet Digital Health, 3 (6), e397-e407. doi: 10.1016/S2589-7500(21)00058-3

Big data and predictive modelling for the opioid crisis: existing research and future potential

2021

Journal Article

The effect of person, treatment and prescriber characteristics on retention in opioid agonist treatment: a 15-year retrospective cohort study

Bharat, Chrianna, Larney, Sarah, Barbieri, Sebastiano, Dobbins, Timothy, Jones, Nicola R., Hickman, Matthew, Gisev, Natasa, Ali, Robert and Degenhardt, Louisa (2021). The effect of person, treatment and prescriber characteristics on retention in opioid agonist treatment: a 15-year retrospective cohort study. Addiction, 116 (11), 3139-3152. doi: 10.1111/add.15514

The effect of person, treatment and prescriber characteristics on retention in opioid agonist treatment: a 15-year retrospective cohort study

2020

Journal Article

Measuring dementia incidence within a cohort of 267,153 older Australians using routinely collected linked administrative data

Welberry, Heidi J., Brodaty, Henry, Hsu, Benjumin, Barbieri, Sebastiano and Jorm, Louisa R. (2020). Measuring dementia incidence within a cohort of 267,153 older Australians using routinely collected linked administrative data. Scientific Reports, 10 (1) 8781, 1. doi: 10.1038/s41598-020-65273-w

Measuring dementia incidence within a cohort of 267,153 older Australians using routinely collected linked administrative data

2020

Journal Article

Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

Barbieri, Sebastiano, Kemp, James, Perez-Concha, Oscar, Kotwal, Sradha, Gallagher, Martin, Ritchie, Angus and Jorm, Louisa (2020). Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk. Scientific Reports, 10 (1) 1111, 1. doi: 10.1038/s41598-020-58053-z

Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

2020

Journal Article

Impact of Prior Home Care on Length of Stay in Residential Care for Australians With Dementia

Welberry, Heidi J., Brodaty, Henry, Hsu, Benjumin, Barbieri, Sebastiano and Jorm, Louisa R. (2020). Impact of Prior Home Care on Length of Stay in Residential Care for Australians With Dementia. Journal of the American Medical Directors Association, 21 (6), 843-850.e5. doi: 10.1016/j.jamda.2019.11.023

Impact of Prior Home Care on Length of Stay in Residential Care for Australians With Dementia

2020

Journal Article

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI

Barbieri, Sebastiano, Gurney-Champion, Oliver J., Klaassen, Remy and Thoeny, Harriet C. (2020). Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI. Magnetic Resonance in Medicine, 83 (1), 312-321. doi: 10.1002/mrm.27910

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI

2017

Journal Article

Selection for biopsy of kidney transplant patients by diffusion-weighted MRI

Steiger, Philipp, Barbieri, Sebastiano, Kruse, Anja, Ith, Michael and Thoeny, Harriet C. (2017). Selection for biopsy of kidney transplant patients by diffusion-weighted MRI. European Radiology, 27 (10), 4336-4344. doi: 10.1007/s00330-017-4814-z

Selection for biopsy of kidney transplant patients by diffusion-weighted MRI

2017

Journal Article

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI

Barbieri, Sebastiano, Brönnimann, Michael, Boxler, Silvan, Vermathen, Peter and Thoeny, Harriet C. (2017). Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI. European Radiology, 27 (4), 1547-1555. doi: 10.1007/s00330-016-4449-5

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI

Availability

Associate Professor Sebastiano Barbieri is:
Available for supervision

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Available projects

Supervision history

Current supervision

  • Doctor Philosophy

    NINA national infrastructure for digital health

    Principal Advisor

    Other advisors: Professor Clair Sullivan

  • Master Philosophy

    Investigating the Impact of Artificial Intelligence on Clinical Workflows, Efficiency, and Health Economics: Implementation Insights, Bias Evaluation, and Strategic Mitigation strategies for Clinical AI Integration

    Principal Advisor

    Other advisors: Professor Ian Scott, Dr Anton van Der Vegt

Enquiries

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communications@uq.edu.au