ORCID as entered in ROS
https://orcid.org/0000-0002-8823-7090
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Preprints
, 2024, Harmonising the Clinical Melody: Tuning Large Language Models for Hospital Course Summarisation in Clinical Coding, http://arxiv.org/abs/2409.14638v2
, 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
, 2023, Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit, http://arxiv.org/abs/2304.07025v1
, 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
, 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
, 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
, 2022, Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models, http://dx.doi.org/10.1016/j.artmed.2023.102662
, 2022, Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding, http://dx.doi.org/10.1016/j.jbi.2022.104161
, 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
, 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
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https://orcid.org/0000-0002-8823-7090