Rate My Professor Irena Spasić

The cornerstone of Professor Spasić's research career is text mining, pivotal for extracting knowledge from big data to inform interventions and decision-making across disciplines, with prominent applications in health and life sciences. Her expertise encompasses text classification, information extraction, term recognition, sentiment analysis, named entity recognition, information retrieval, and language resources. Additional interests include knowledge representation through ontologies, machine learning techniques such as support vector machines and genetic algorithms, and information management involving data modeling and mining. As co-founder of HealTex, the UK Healthcare Text Analytics Network, she has driven interdisciplinary efforts to leverage healthcare free text in research and practice. Notable publications include 'A spatial analysis of the use of Bitcoin as a medium of exchange' (2025, Financial Innovation), 'Deep learning approaches to automatic radiology report generation: A systematic review' (2023, Informatics in Medicine Unlocked), 'Simulation and annotation of global acronyms' (2022, Bioinformatics), 'The value of numbers in clinical text classification' (2023, Machine Learning and Knowledge Extraction), and 'Text mining of cancer-related information: review of current approach and trends' (2014). Her contributions have fostered collaborations extending impact beyond computer science into clinical and social domains.