By Mr Nic Kuo

Book Chapters

Wong ZSY; Waters N; Agchbayar A; Batsaikhan B; Enkhbold T; Batzorig K; Ganzorig O; Kuo NIH; Liu J, 2024, 'Rule-Based Natural Language Processing Pipeline to Detect Medication-Related Named Entities: Insights for Transfer Learning', in , pp. 584 - 588, http://dx.doi.org/10.3233/SHTI231032

Journal articles

Marchesi R; Micheletti N; I-Hsien Kuo N; Barbieri S; Jurman G; Osmani V, 2025, 'Generative AI mitigates representation bias and improves model fairness through synthetic health data', Plos Computational Biology, 21, http://dx.doi.org/10.1371/journal.pcbi.1013080

Kuo NIH; 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, 2024, 'Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project', Jmir Medical Education, 10, http://dx.doi.org/10.2196/51388

Kuo NIH; Garcia F; Sönnerborg A; Böhm M; Kaiser R; Zazzi M; 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', Journal of Biomedical Informatics, 144, http://dx.doi.org/10.1016/j.jbi.2023.104436

Kuo NIH; Polizzotto MN; Finfer S; Garcia F; Sönnerborg A; Zazzi M; Böhm M; Kaiser R; Jorm L; Barbieri S, 2022, 'The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms', Scientific Data, 9, http://dx.doi.org/10.1038/s41597-022-01784-7

Conference Papers

Kuo NIH; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2021, 'Plastic and stable gated classifiers for continual learning', in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 3548 - 3553, http://dx.doi.org/10.1109/CVPRW53098.2021.00394

I-Hsien Kuo N; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2021, 'Learning to Continually Learn Rapidly from Few and Noisy Data', in Proceedings of Machine Learning Research, pp. 65 - 76

Kuo NIH; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2020, 'An Input Residual Connection for Simplifying Gated Recurrent Neural Networks', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN48605.2020.9207238

Kuo NIH; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2020, 'M2SGD: Learning to learn important weights', in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 957 - 964, http://dx.doi.org/10.1109/CVPRW50498.2020.00126

Chao X; El-Khaissi C; Kuo N; John PK; Suominen H, 2020, 'An Automatic Vowel Space Generator for Language Learners’ Pronunciation Acquisition and Correction', in Proceedings of the Australasian Language Technology Workshop

Preprints

Kuo NI-H; Gallego B; Jorm L, 2025, Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records, http://dx.doi.org/10.48550/arxiv.2503.06096

Kuo NI-H; Gallego B; Jorm L, 2024, CK4Gen: A Knowledge Distillation Framework for Generating High-Utility Synthetic Survival Datasets in Healthcare, http://dx.doi.org/10.48550/arxiv.2410.16872

Kuo NI-H; Gallego B; Jorm L, 2024, Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation, http://dx.doi.org/10.48550/arxiv.2410.16811

Marchesi R; Micheletti N; Kuo NI-H; Barbieri S; Jurman G; Osmani V, 2023, Generative AI Mitigates Representation Bias and Improves Model Fairness Through Synthetic Health Data, http://dx.doi.org/10.1101/2023.09.26.23296163

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, 2023, 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

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, 2022, 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

Kuo NI-H; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2021, Learning to Continually Learn Rapidly from Few and Noisy Data, http://dx.doi.org/10.48550/arxiv.2103.04066

Kuo NI-H; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2020, MTL2L: A Context Aware Neural Optimiser, http://dx.doi.org/10.48550/arxiv.2007.09343

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