By Dr Sebastiano Barbieri

Preprints

Zhou G; Catic A; Shabestari M; Young M; Li C; Poppe K; Barbieri S, 2026, From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories, http://dx.doi.org/10.48550/arxiv.2603.06720

Kuppens D; Barbieri S; Berg DVD; Schouten P; Thoeny HC; Wennen M; Gurney-Champion OJ, 2024, Acquisition-Independent Deep Learning for Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations, http://dx.doi.org/10.48550/arxiv.2412.20844

Kuo NI-H; Jorm L; Barbieri S, 2023, Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models, http://dx.doi.org/10.48550/arxiv.2303.12281

Kuo NI-H; Garcia F; Sönnerborg A; Zazzi M; Böhm M; Kaiser R; Polizzotto M; Jorm L; Barbieri S, 2023, Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV, http://dx.doi.org/10.48550/arxiv.2208.08655

Kuo NI-H; Polizzotto MN; Finfer S; Garcia F; Sönnerborg A; Zazzi M; Böhm M; Jorm L; Barbieri S, 2022, The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms, http://dx.doi.org/10.48550/arxiv.2203.06369

Kuo NI-H; Polizzotto M; Finfer S; Jorm L; Barbieri S, 2021, Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project, http://dx.doi.org/10.48550/arxiv.2112.03914

Liu J; Gallego B; Barbieri S, 2021, Incorporating Uncertainty in Learning to Defer Algorithms for Safe Computer-Aided Diagnosis, http://dx.doi.org/10.48550/arxiv.2108.07392

Kaandorp MPT; Barbieri S; Klaassen R; van Laarhoven HWM; Crezee H; While PT; Nederveen AJ; Gurney-Champion OJ, 2021, Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients, http://dx.doi.org/10.48550/arxiv.2011.01689

Barbieri S; Mehta S; Wu B; Bharat C; Poppe K; Jorm L; Jackson R, 2020, Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach, http://dx.doi.org/10.48550/arxiv.2011.14032

Barbieri S; Kemp J; Perez-Concha O; Kotwal S; Gallagher M; Ritchie A; Jorm L, 2020, Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk, http://dx.doi.org/10.48550/arxiv.1905.08547

Barbieri S; Gurney-Champion OJ; Klaassen R; Thoeny HC, 2019, Deep Learning How to Fit an Intravoxel Incoherent Motion Model to Diffusion-Weighted MRI, http://dx.doi.org/10.48550/arxiv.1903.00095

Bauer MHA; Barbieri S; Klein J; Egger J; Kuhnt D; Freisleben B; Hahn HK; Nimsky C, 2013, A Ray-based Approach for Boundary Estimation of Fiber Bundles Derived from Diffusion Tensor Imaging, http://dx.doi.org/10.48550/arxiv.1310.6092

Klein J; Barbieri S; Bauer MHA; Nimsky C; Hahn HK, 2011, Benchmarking the Quality of Diffusion-Weighted Images, http://dx.doi.org/10.48550/arxiv.1104.1556

Bauer MHA; Egger J; Kuhnt D; Barbieri S; Klein J; Hahn HK; Freisleben B; Nimsky C, 2011, Ray-Based and Graph-Based Methods for Fiber Bundle Boundary Estimation, http://dx.doi.org/10.48550/arxiv.1103.1952

Bauer MHA; Egger J; Kuhnt D; Barbieri S; Klein J; Hahn HK; Freisleben B; Nimsky C, 2011, A Semi-Automatic Graph-Based Approach for Determining the Boundary of Eloquent Fiber Bundles in the Human Brain, http://dx.doi.org/10.48550/arxiv.1103.1475

Carson JM; Barbieri S; Verich A; Tu E; Lloyd A; Dore GJ; Matthews GV; Martinello M, Development of a machine learning model to predict short duration HCV treatment response, http://dx.doi.org/10.1101/2025.09.19.25336147

Kuo NI-H; Perez-Concha O; Hanly M; Mnatzaganian E; Hao B; Di Sipio M; Yu G; Vanjara J; Valerie IC; de Oliveira Costa J; Churches T; Lujic S; Hegarty J; Jorm L; Barbieri S, Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project (Preprint), http://dx.doi.org/10.2196/preprints.51388

Marchesi R; Micheletti N; Kuo NI-H; Barbieri S; Jurman G; Osmani V, Generative AI Mitigates Representation Bias and Improves Model Fairness Through Synthetic Health Data, http://dx.doi.org/10.1101/2023.09.26.23296163

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