By Professor Blanca Gallego Luxan

Conference Papers

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, https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000168477900057&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=891bb5ab6ba270e68a29b250adbe88d1

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 BV, 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

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

Greenberg JD; Huberts LCE; Ritchie A; Ooi S-Y; Flynn GM; Hart GK; Gallego B, 2024, 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

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

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

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

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

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

Hwang H; Quiroz JC; Gallego B, 2021, Assessing The Effectiveness of Empirical Calibration Under Different Bias Scenarios, http://dx.doi.org/10.21203/rs.3.rs-1058822/v1

Hwang H; Quiroz JC; Gallego B, 2021, Assessing the effectiveness of empirical calibration under different bias scenarios, http://dx.doi.org/10.48550/arxiv.2111.04233

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

Zhu J; Gallego B, 2021, 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

Kennedy G; Gallego B, 2021, Clinician Readiness to Adopt A.I. for Critical Care Prioritisation, http://dx.doi.org/10.1101/2021.02.11.21251604

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

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

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

Zhu J; Gallego B, 2020, 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

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

Castelyn G; Laranjo L; Schreier G; Gallego B, 2020, 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

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/10.1016/j.jbi.2020.103474

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

Other

Hopkins T; Messum S; Deady L; Dimitri A; Soars L; Gallego-Luxan B; McKenzie D; Sands M, 2017, Advance Care Planning (ACP) for end stage respiratory patients identified using the Hospital One year Mortality Rating (HOMR) tool, European Respiratory Society

Luxan BG, 1995, Modelo de bases vibracionales contraidas aplicado a los modos de torsion y aleteo de la molecula de metilamina, Universidad Autonoma de Madrid, Facultad de Ciencias

Back to profile page