By Professor Blanca Gallego Luxan

Journal articles

Lenzen M; Gallego B; Wood R, 2006, 'A flexible approach to matrix balancing under partial information', Journal of Applied Input-Output Analysis, 11, pp. 1 - 24

Peters G; Sack F; Lenzen M; Lundie S; Gallego B, 2006, 'A new ecological footprint calculation for the australian water industry: Regionalisation and inclusion of downstream impacts', J. Appl. Input-Output Analysis, 12, pp. 73 - 90

Lenzen M; Dey C; Foran B; Gallego B; Murray J; Ward C; Wood R; Blackman E; Cole J; Sack F, 2006, 'Triple-bottom-line accounting of society, economy and environment-a new life-cycle software for business',

Gallego B; Lenzen M, 2005, 'A consistent input–output formulation of shared producer and consumer responsibility', Economic Systems Research, 17, pp. 365 - 391

Gallego B; Cessi P; McWilliams JC, 2004, 'The Antarctic Circumpolar Current in equilibrium', Journal of physical oceanography, 34, pp. 1571 - 1587

Gallego B, 2002, 'Simple models of large-scale midlatitude ocean-atmosphere dynamics', PhDT

Gallego B; Cessi P, 2001, 'Decadal variability of two oceans and an atmosphere', Journal of Climate, 14, pp. 2815 - 2832

Meléndez FJ; Gallego‐Luxan B; Demaison J; Smeyers YG, 2000, 'Ab initio determination of the infrared phosphine torsion spectrum in vinylphosphine with ZPE correction', Journal of Computational Chemistry, 21, pp. 1167 - 1175

Gallego B; Cessi P, 2000, 'Exchange of heat and momentum between the atmosphere and the ocean: a minimal model of decadal oscillations', Climate Dynamics, 16, pp. 479 - 489

Gallego B, 1999, 'Decadal Oscillations in the Mid-Latitude Ocean-Atmosphere System', Astrophysical and Geophysical Flows as Dynamical Systems 1998 Summer Study Program in Geophysical Fluid Dynamics, pp. 277

Iglesias T; Noheda B; Gallego B; del Castillo JRF; Lifante G; Gonzalo JA, 1994, 'Scaling equation of state for ferroelectric triglycine selenate at T≈ Tc', EPL (Europhysics Letters), 28, pp. 91

Karatzas KD; Iliadis L; Spartalis S; Bahrepour M; Meratnia N; Havinga PJM; Sezer A; Goktepe AB; Altun S; Yu T, 'Artificial Intelligence Applications in Environmental',

Conference Papers

Kennedy G; Dras M; Gallego B, 2021, 'Augmentation of Electronic Medical Record Data for Deep Learning', in Studies in Health Technology and Informatics, Virtual event, pp. 582 - 586, presented at 18th World Congress of Medical and Health Informatics, MedInfo 2021, Virtual event, 02 October 2021 - 04 October 2021, http://dx.doi.org/10.3233/SHTI220144

Schuler T; Liu T; Lamoury G; Morgia M; Carroll S; Hruby G; Kneebone A; Eade T; Brown C; Gallego B, 2018, 'Comparing comprehensiveness and accuracy of data between radiotherapy databases for breast and prostate cancer at the same institution', 2018 Annual Scientific Meeting

Schuler T; Hruby G; Kwong C; Clarke S; Gallego B; Eade T, 2018, 'Rationale and design of the Tracker Study: Exploring feasibility of real-time activity and weight monitoring during routine care of oncology patients', European Congress of Radiology-2018 ASM

Sivaraman V; Carrapetta J; Hu K; Luxan BG, 2013, 'HazeWatch: A participatory sensor system for monitoring air pollution in Sydney', in 38th Annual IEEE Conference on Local Computer Networks-Workshops, IEEE, pp. 56 - 64

Luxan BG; Magrabi F; Concha OP; Wang Y; Coiera E, 2013, 'Insights into patterns of healthcare delivery from hospital electronic medical records', in HISA Big Data in Biomedicine and Healthcare 2013 conference, Health Informatics Society of Australia

Dunn AG; Gallego Luxan B, 2010, 'Prescription volumes show that primary care is slow to respond to negative evidence, ACSQHC, 6-8 September, Perth.', Perth, presented at 8th Australasian Conference on Safety and Quality in Healthcare, Perth, 06 September 2010 - 08 September 2010

Yu T; Lenzen M; Gallego B; Debenham JK, 2009, 'A Data Mining System for Estimating a Largesize Matrix for the Environmental Accounting.', in AIAI Workshops, Citeseer, pp. 260 - 269

Akhtar M; Gallego B; Shiue AY; Sintchenko V, 2009, 'Prospective Biosurveillance for Early Detection of Disease Outbreaks', in HIC 2009: Proceedings; Frontiers of Health Informatics-Redefining Healthcare, National Convention Centre Canberra, 19-21 August 2009, Health Informatics Society of Australia (HISA), pp. 132

Sintchenko V; Gallego B; Chung G; Coiera E, 2009, 'Towards bioinformatics assisted infectious disease control', in BMC bioinformatics, BioMed Central, pp. 1 - 9

Lenzen M; Wood R; Gallego B, 2006, 'RAS matrix balancing under conflicting information', in Intermediate Input-Output Meetings

Gallego B; Cessi P, 1999, 'Decadal oscillations in the mid-latitude ocean-atmosphere system', in 12TH CONFERENCE ON ATMOSPHERIC AND OCEANIC FLUID DYNAMICS, AMER METEOROLOGICAL SOCIETY, NY, NEW YORK, pp. 188 - 192, presented at 12th Conference on Atmospheric and Oceanic Fluid Dynamics, NY, NEW YORK, 07 June 1999 - 11 June 1999

X. Cai, O. Perez-Concha, F. Martin-Sanchez, B. Gallego. , 'Modelling of Time Series Health Data using Dynamic Bayesian Networks: An application to predictions of patient outcomes after multiple surgeries.', in Proceedings of the Big Data Conference 2014, Melbourne

Conference Presentations

Dunn A; Gallego Luxan B, 2009, 'Explaining low levels of recommended care in healthcare provision via diffusion through advice-giving networks', presented at S4 Conference on Emergence in Geographical Space, Paris, France, 23 November 2009 - 25 November 2009

Conference Abstracts

Jin X; Ding C; Hunter D; Gallego B, 2022, 'EFFECTIVENESS OF VITAMIN D SUPPLEMENTATION ON KNEE OSTEOARTHRITIS - A TARGET TRIAL EMULATION STUDY USING DATA FROM THE OSTEOARTHRITIS INITIATIVE COHORT', in Osteoarthritis and Cartilage, Elsevier, Vol. 30, pp. s63 - s64, http://dx.doi.org/10.1016/j.joca.2022.02.074

Reports

Smith GC; Vo K; Nathan S; Carland JE; Uebel K; Hieu Dinh H; Shulruf B; Torda A; Kennedy S; O'Sullivan AJ; Palit V; Gallego Luxan B; Velan G; Biles B, 2022, Re-imagining medical student research education - University of New South Wales, Medical Deans Australia and New Zealand Inc., Volume 1, http://dx.doi.org/10.26190/unsworks/28474, https://medicaldeans.org.au/md/2022/06/Research-in-the-Medical-Curriculum-Volume-1-A-window-on-innovation-and-good-practice-2022.pdf

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

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