By Professor Louisa Jorm

Preprints

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

Blake V; Jorm L; Yu J; Lee A; Gallego B; Ooi S-Y, 2023, The Cardiac Analytics and Innovation (CardiacAI) Data Repository: An Australian data resource for translational cardiovascular research, , http://dx.doi.org/10.48550/arxiv.2304.09341

Fitzgerald O; Perez-Concha O; Gallego-Luxan B; Metke-Jimenez A; Rudd L; Jorm L, 2023, Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit, , http://arxiv.org/abs/2304.07025v1

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

Liu L; Perez-Concha O; Nguyen A; Bennett V; Blake V; Gallego B; Jorm L, 2023, Web-Based Application Based on Human-in-the-Loop Deep Learning for Deidentifying Free-Text Data in Electronic Medical Records: Development and Usability Study (Preprint), , http://dx.doi.org/10.2196/preprints.46322

Fitzgerald O; Perez-Concha O; Gallego-Luxan B; Rudd L; Jorm L, 2023, Curation and description of a blood glucose management and nutritional support cohort using the eICU collaborative research database, , http://dx.doi.org/10.1101/2023.04.20.23288845

Fitzgerald O; Perez-Concha O; Gallego-Luxan B; Rudd L; Jorm L, 2023, The relationship between hyperglycaemia on admission and patient outcome is modified by hyperlactatemia and diabetic status: a retrospective analysis of the eICU collaborative research database, , http://dx.doi.org/10.1101/2023.05.01.23289339

Liu L; Perez-Concha O; Nguyen A; Bennett V; Jorm L, 2022, Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models, , http://dx.doi.org/10.1016/j.artmed.2023.102662

Quiroz JC; Brieger D; Jorm L; Sy RW; Hsu B; Gallego B, 2022, Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation, , http://dx.doi.org/10.1016/j.hlc.2023.12.016

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

Liu L; Perez-Concha O; Nguyen A; Bennett V; Jorm L, 2022, Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding, , http://dx.doi.org/10.1016/j.jbi.2022.104161

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

Butler DC; Jorm LR; Larkins S; Korda RJ, 2022, Area-level and individual-socioeconomic variation in use of GP and specialist services. A multilevel analysis using linked data, , http://dx.doi.org/10.21203/rs.3.rs-1428954/v1

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

Quiroz J; Chard T; Sa Z; Ritchie A; Jorm L; Gallego B, 2021, Extract, Transform, Load Framework for the Conversion of Health Databases to OMOP, , http://dx.doi.org/10.1101/2021.04.08.21255178

Liu L; Perez-Concha O; Nguyen A; Bennett V; Jorm L, 2020, De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models, , http://dx.doi.org/10.48550/arxiv.2101.00146

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, 2019, Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk, , http://dx.doi.org/10.48550/arxiv.1905.08547

Back to profile page