Karin Verspoor

Professor Karin Verspoor is Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia.

Karin's research primarily focuses on the use of artificial intelligence methods to enable biological discovery and clinical decision support, through extraction of information from clinical texts and the biomedical literature and machine learning-based modelling.

Karin held previous posts as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, as the Scientific Director of Health and Life Sciences at NICTA Victoria Research Laboratory, at the University of Colorado School of Medicine, and at Los Alamos National Laboratory.

She is also the Victorian Node lead and co-founder of the Australian Alliance for Artificial Intelligence in Health.

Key Activities

• Artificial Intelligence in Medicine
• Biomedical Natural Language Processing
• Computational Linguistics
• Health Informatics
• Health Data Analytics
• Computational Biology
• Cheminformatics

Qualifications

• PhD, Cognitive Science and Natural Language, The University of Edinburgh (UK)
• MSc, Cognitive Science and Natural Language, The University of Edinburgh (UK)
• BA, Computer Science, Rice University (Houston, TX, USA)

Industry Experience

• Intelligenesis/Webmind Corporation (New York, NY, USA)
• Applied Semantics (Los Angeles, CA, USA)
• Los Alamos National Laboratory (Los Alamos, NM, USA)
• National ICT Australia (Melbourne, VIC, Australia)

Publications

• Elangovan, A.,Li, Y.,Pires, D.,Davis, M.,Verspoor, K. (2022). Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT In: BMC Bioinformatics, 23, 1 - 23
• Rozova, V.,Witt, K.,Robinson, J.,Li, Y.,Verspoor, K. (2022). Detection of self-harm and suicidal ideation in emergency department triage notes In: Journal of the American Medical Informatics Association : JAMIA, 29, 472 - 480
• Liu, Y.,Meric, G.,Havulinna, A.,Verspoor, K., et al, . (2022). Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting In: Cell Metabolism, 34, 719 - 730
• Hamed, A.,Fandy, T.,Tkaczuk, K.,Verspoor, K.,Lee, B. (2022). COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments In: Pharmaceutics, 14, 1 - 18
• Hur, B.,Hardefeldt, L.,Verspoor, K.,Baldwin, T.,Gilkerson, J. (2022). Evaluating the dose, indication and agreement with guidelines of antimicrobial use in companion animal practice with natural language processing In: JAC - Antimicrobial Resistance, , 1 - 9
• Hur, B.,Hardefeldt, L.,Verspoor, K.,Baldwin, T.,Gilkerson, J. (2022). Overcoming challenges in extracting prescribing habits from veterinary clinics using big data and deep learning In: Australian Veterinary Journal, 100, 220 - 222
• Valentine, J.,Hall, L.,Verspoor, K.,Gillespie, E.,Worth, L. (2022). Use of a Victorian statewide surveillance program to evaluate the burden of healthcare-associated Staphylococcus aureus bacteraemia and Clostridioides difficile infection in patients with cancer In: Internal Medicine Journal, 52, 1215 - 1224
• Verspoor, C. (2022). Cross-modal Clinical Graph Transformer For Ophthalmic Report Generation In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, United States, 19/06/2022 - 24/06/2022
• Li, Y.,Fang, B.,He, J.,Verspoor, K., et al, . (2022). The ChEMU 2022 Evaluation Campaign: Information Extraction in Chemical Patents In: Advances in Information Retrieval. ECIR 2022, Stavanger, Norway, 10-14 April 2022
• Wang, Y.,Beck, D.,Baldwin, T.,Verspoor, C. (2022). Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression In: Transactions of the Association for Computational Linguistics, 10, 680 - 696

Grants

• Improving patient outcomes through implementation of digital and diagnostic innovations for infections in cancer (Peter Mac led). Funded by: National Health and Medical Research Council Synergy Grants from (2022 to 2027)
• Technology Development for improved Clinical Trials prediction models (administered by Opyl Limited). Funded by: Innovation Connections grant - Cat 1 from (2022 to 2023)
• Using artificial intelligence and novel technology to detect and monitor corneal disease (administered by University of Melbourne). Funded by: National Health and Medical Research Council (NHMRC) Project Grants 2017 (for funding commencing in 2018) from (2021 to 2023)
• Automated Assessment of Data Quality in Biological Knowledge Resources (Administered by University of Melbourne). Funded by: ARC Discovery Projects 2019 from (2021 to 2021)
• Real-time clinical decision support - Natural Language Processing Supporting Clinical Reasoning (Administered by University of Melbourne). Funded by: ARC Industrial Transformation Training Centre via other University Grant 2017 from (2020 to 2021)