By Professor Blanca Gallego Luxan

Preprints

Gallego B; Huberts L; Yu J; Blake V; Jorm L; Liu L; Ooi S-Y, 2026, Explainable, personalised prediction of emergency readmission and mortality following hospitalisation in patients with heart failure, http://dx.doi.org/10.64898/2026.07.15.26358201

Kuo NI-H; Gallego B; Jorm L, 2026, From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model, http://dx.doi.org/10.48550/arxiv.2606.08945

Kuo NI-H; Gallego B; Jorm L, 2026, Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records, http://dx.doi.org/10.48550/arxiv.2606.08903

Blake V; Novak J; Miller M; Ooi S-Y; Gallego B, 2026, CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications, http://dx.doi.org/10.48550/arxiv.2602.17949

Kuo NI-H; Tania MH; Gallego B; Jorm L, 2026, PRIME-CVD: A Parametrically Rendered Informatics Medical Environment for Education in Cardiovascular Risk Modelling, http://dx.doi.org/10.48550/arxiv.2603.19299

Kuo NI-H; Gallego B; Jorm L, 2025, Limits of Generative Pre-Training in Structured EMR Trajectories with Irregular Sampling, http://dx.doi.org/10.48550/arxiv.2510.22878

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, Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation, http://dx.doi.org/10.48550/arxiv.2410.16811

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

Quiroz JC; Brieger D; Jorm L; Sy RW; Hsu B; Gallego B, 2023, Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation, http://dx.doi.org/10.48550/arxiv.2211.11965

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://dx.doi.org/10.48550/arxiv.2304.07025

Zhu J; Gallego B, 2022, Causal inference for observational longitudinal studies using deep survival models, http://dx.doi.org/10.48550/arxiv.2101.10643

Hwang H; Quiroz JC; Gallego B, 2022, Assessing the effectiveness of empirical calibration under different bias scenarios, http://dx.doi.org/10.48550/arxiv.2111.04233

Sakal C; Hwang H; Quiroz JC; Gallego B, 2022, TreatmentEstimatoR: a Dashboard for Estimating Treatment Effects from Observational Health Data, http://dx.doi.org/10.48550/arxiv.2203.10458

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

Gallego B; Zhu J, 2021, Time-to-event comparative effectiveness of NOACs vs VKAs in newly diagnosed non-valvular atrial fibrillation patients, http://dx.doi.org/10.1101/2021.08.06.21261092

Deng X; Feng Y; Sutjarittham T; Gharakheili HH; Gallego B; Sivaraman V, 2021, Comparing Broadband ISP Performance using Big Data from M-Lab, http://dx.doi.org/10.48550/arxiv.2101.09795

Quiroz JC; Brieger D; Jorm L; Sy RW; Falster MO; Gallego B, 2021, An observational study of clinical and health system factors associated with catheter ablation and early ablation treatment for atrial fibrillation in Australia, http://dx.doi.org/10.1101/2021.09.03.21263104

Khalifa M; Magrabi F; Gallego B, 2020, Validating and Updating GRASP: An Evidence-Based Framework for Grading and Assessment of Clinical Predictive Tools, http://dx.doi.org/10.21203/rs.3.rs-15929/v2

Kennedy G; Rihari-Thomas J; Dras M; Gallego B, 2020, Developing a deep learning system to drive the work of the critical care outreach team, http://dx.doi.org/10.1101/2020.07.07.20148064

Khalifa M; Magrabi F; Gallego B, 2020, Validating and Updating GRASP: An Evidence-Based Framework for Grading and Assessment of Clinical Predictive Tools, http://dx.doi.org/10.21203/rs.3.rs-15929/v1

Zhu J; Gallego B, 2019, Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis, http://dx.doi.org/10.48550/arxiv.1910.08877

Khalifa M; Magrabi F; Gallego B, 2019, Evaluating the Impact of Using GRASP Framework on Clinicians and Healthcare Professionals Decisions in Selecting Clinical Predictive Tools, http://dx.doi.org/10.48550/arxiv.1907.11523

Khalifa M; Magrabi F; Gallego B, 2019, Developing an Evidence-Based Framework for Grading and Assessment of Predictive Tools for Clinical Decision Support, http://dx.doi.org/10.48550/arxiv.1907.03706

Khalifa M; Magrabi F; Gallego B, 2019, Validating and Updating GRASP: A New Evidence-Based Framework for Grading and Assessment of Clinical Predictive Tools

Zhou N; Gallego B; Bao J; Tsafnat G, 2017, Pan-cancer scale landscape of simple somatic mutations, http://dx.doi.org/10.1101/112367

Hwang H; Quiroz JC; Gallego B, Assessing The Effectiveness of Empirical Calibration Under Different Bias Scenarios, http://dx.doi.org/10.21203/rs.3.rs-1058822/v1

Kennedy G; Gallego B, Clinician Readiness to Adopt A.I. for Critical Care Prioritisation, http://dx.doi.org/10.1101/2021.02.11.21251604

Fitzgerald O; Perez-Concha O; Gallego-Luxan B; Rudd L; Jorm L, 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

Khalifa M; Magrabi F; Gallego Luxan B, Evaluating the Impact of the Grading and Assessment of Predictive Tools Framework on Clinicians and Health Care Professionals’ Decisions in Selecting Clinical Predictive Tools: Randomized Controlled Trial (Preprint), http://dx.doi.org/10.2196/preprints.15770

Zhu J; Gallego B, Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants, http://dx.doi.org/10.21203/rs.3.rs-44647/v2

Zhu J; Gallego B, Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants, http://dx.doi.org/10.21203/rs.3.rs-44647/v1

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

Greenberg JD; Huberts LCE; Ritchie A; Ooi S-Y; Flynn GM; Hart GK; Gallego B, Improved Sensitivity For Detection Of Clinical Deterioration When Diagnostic Pathology And Patient Trends Are Included In Machine Learning Models, http://dx.doi.org/10.1101/2024.10.20.24315403

Hallinan CM; Ward R; Hart GK; Sullivan C; Pratt N; Ng AP; Capurro D; Vegt AVD; Liaw T; Daly O; Luxan BG; Bunker D; Boyle D, Integrated Data Governance, Digital Health, and the Common Data Model (OMOP-CDM), http://dx.doi.org/10.21203/rs.3.rs-3479039/v1

Castelyn G; Laranjo L; Schreier G; Gallego B, Predictive performance and impact of algorithms in remote monitoring of chronic conditions: a systematic review and meta-analysis (Preprint), http://dx.doi.org/10.2196/preprints.19253

Kuo NI-H; Tania MH; Gallego B; Jorm LR, PRIME-CVD: A Parametrically Rendered Informatics Medical Environment for Education in Cardiovascular Risk Modelling, http://dx.doi.org/10.64898/2026.03.19.26348848

Fitzgerald O; Perez-Concha O; Gallego-Luxan B; Rudd L; Jorm L, 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; Blake V; Gallego B; Jorm L, 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

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