Yr Athro Irena Spasic

2022

• Tong, Y., Liang, Y., Spasic, I., Hicks, Y., Hu, H. and Liu, Y. 2022. A data-driven approach for integrating hedonic quality and pragmatic quality in user experience modelling. Journal of Computing and Information Science in Engineering 22(6), article number: 61002. (10.1115/1.4054155)
• Zunic, A., Corcoran, P. and Spasic, I. 2022. The case of aspect in sentiment analysis: seeking attention or co-dependency?. Machine Learning and Knowledge Extraction 4(2), pp. 474-487. (10.3390/make4020021)
• Filimonov, M., Chopard, D. and Spasic, I. 2022. Simulation and annotation of global acronyms. Bioinformatics 38(11), pp. 3136-3138., article number: btac298. (10.1093/bioinformatics/btac298)

2021

• Chopard, D., Treder, M., Corcoran, P., Johnson, C., Busse-Morris, M. and Spasic, I. 2021. Text mining of adverse events in clinical trials: Deep learning approach. JMIR Medical Informatics 9(12), article number: e28632. (10.2196/28632)
• Muralidaran, V., Spasic, I. and Knight, D. 2021. A systematic review of unsupervised approaches to grammar induction. Natural Language Engineering 27(6), pp. 647-689. (10.1017/S1351324920000327)
• Tong, Y., Liang, Y., Liu, Y., Hicks, Y. and Spasic, I. 2021. Integrating hedonic quality for user experience modelling. Presented at: ASME IDETC/CIE 2021, Virtual, 17-20 August 202141st Computers and Information in Engineering Conference (CIE) Proceedings, Vol. 2. ASME, (10.1115/DETC2021-69781)
• Rogers, D., Preece, A., Innes, M. and Spasic, I. 2021. Real-time text classification of user-generated content on social media: Systematic review. IEEE Transactions on Computational Social Systems (10.1109/TCSS.2021.3120138)
• Knight, D., Loizides, F., Neale, S., Anthony, L. and Spasic, I. 2021. Developing computational infrastructure for the CorCenCC corpus - the National Corpus of Contemporary Welsh. Language Resources and Evaluation 55, pp. 789-816. (10.1007/s10579-020-09501-9)
• Zunic, A., Corcoran, P. and Spasic, I. 2021. Aspect-based sentiment analysis with graph convolution over syntactic dependencies. Artificial Intelligence in Medicine 119, article number: 102138. (10.1016/j.artmed.2021.102138)
• Corcoran, P., Palmer, G., Arman, L., Knight, D. and Spasic, I. 2021. Creating Welsh language word embeddings. Applied Sciences 11(15), article number: 6896. (10.3390/app11156896)
• Espinosa-Anke, L., Palmer, G., Filimonov, M., Corcoran, P., Spasic, I. and Knight, D. 2021. English–Welsh cross-lingual embeddings. Applied Sciences 11(14), article number: 6541. (10.3390/app11146541)
• Dunphy, E., Button, K., Hamilton, F., Williams, J., Spasic, I. and Murray, E. 2021. Feasibility randomised controlled trial comparing TRAK-ACL digital rehabilitation intervention plus treatment as usual versus treatment as usual for patients following anterior cruciate ligament reconstruction. BMJ Open Sport and Exercise Medicine 7(2), article number: e001002. (10.1136/bmjsem-2020-001002)
• Setchi, R., Spasic, I., Morgan, J., Harrison, C. and Corken, R. 2021. Artificial intelligence for patent prior art searching. World Patent Information 64, article number: 102021. (10.1016/j.wpi.2021.102021)
• Hughes, C., Filimonov, M., Wray, A. and Spasic, I. 2021. Leaving no stone unturned: flexible retrieval of idiomatic expressions from a large text corpus. Machine Learning and Knowledge Extraction 3(1), pp. 263-283. (10.3390/make3010013)
• Palmer, G., Corcoran, P., Arman, L., Knight, D. and Spasic, I. 2021. A closer look at Welsh word embeddings. In: Prys, D. ed. Language and Technology in Wales: Volume 1. Bangor: Bangor University, pp. 21-29.
• Muralidaran, V., Palmer, G., Arman, L., O'Hare, K., Knight, D. and Spasic, I. 2021. A practical implementation of a porter stemmer for Welsh. In: Prys, D. ed. Language and Technology in Wales: Volume 1. Bangor: Bangor University, pp. 30-43.

2020

• Spasic, I. and Button, K. 2020. Patient triage by topic modelling of referral letters: Feasibility study. JMIR Medical Informatics 8(11), article number: e21252. (10.2196/21252)
• Muralidaran, V., Spasic, I. and Knight, D. 2020. A cognitive approach to parsing with neural networks. Presented at: International Conference on Statistical Language and Speech Processing (SLSP), Cardiff, UK, 14–16 Oct 2020Statistical Language and Speech Processing, Vol. 12379. Springer Verlag pp. 71-84., (10.1007/978-3-030-59430-5_6)
• Owen, D. et al. 2020. Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections.[Version 2]. Research Ideas and Outcomes 6, article number: e58030. (10.3897/rio.6.e58030)
• Owen, D. et al. 2020. Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections [Version 1]. Research Ideas and Outcomes 6, article number: e55789. (10.3897/rio.6.e55789)
• Spasic, I., Williams, L. and Buerki, A. 2020. Idiom–based features in sentiment analysis: cutting the Gordian knot. IEEE Transactions on Affective Computing 11(2) (10.1109/TAFFC.2017.2777842)
• Setchi, R. and Spasic, I. 2020. AI-assisted patent prior art searching - feasibility study. The Intellectual Property Office. Available at: https://www.gov.uk/government/publications/ai-assisted-patent-prior-art-searching-feasibility-study
• Spasic, I. and Nenadic, G. 2020. Clinical text data in machine learning: Systematic review. JMIR Medical Informatics 8(3), article number: e17984. (10.2196/17984)
• Spasic, I., Uzuner, ?. and Zhou, L. 2020. Emerging clinical applications of text analytics. International Journal of Medical Informatics 134, article number: 103974. (10.1016/j.ijmedinf.2019.103974)
• Button, K., Spasic, I., Playle, R., Owen, D., Lau, M., Hannaway, L. and Jones, S. 2020. Using routine referral data for patients with knee and hip pain to improve access to specialist care. BMC Musculoskeletal Disorders 21, article number: 66. (10.1186/s12891-020-3087-x)
• Zunic, A., Corcoran, P. and Spasic, I. 2020. Sentiment analysis in health and wellbeing: A systematic review. JMIR Medical Informatics 8(1), article number: e16023. (10.2196/16023)
• Dai, X., Spasic, I., Chapman, S. and Meyer, B. 2020. The state of the art in implementing machine learning for mobile apps: A survey. Presented at: IEEE SoutheastCon, Raleigh, USA, 12-15 Mar 2020. pp. -.
• Zunic, A., Corcoran, P. and Spasic, I. 2020. Improving the performance of sentiment analysis in health and wellbeing using domain knowledge. Presented at: the Third UK Healthcare Text Analytics Conference (HealTAC), London, UK, 22-24 April 2020. pp. -.
• Espinosa-Anke, L., Martin-Vide, C. and Spasic, I. eds. 2020. Statistical language and speech processing: 8th International Conference, SLSP 2020, Cardiff, UK, October 14–16, 2020. Springer.

2019

• Spasic, I., Krzeminski, D., Corcoran, P. and Balinsky, A. 2019. Cohort selection for clinical trials from longitudinal patient records: text mining approach. JMIR Medical Informatics 7(4), article number: e15980. (10.2196/15980)
• Spasic, I., Owen, D., Smith, A. and Button, K. 2019. KLOSURE: Closing in on open–ended patient questionnaires with text mining. Journal of Biomedical Semantics 10(S1), article number: 24. (10.1186/s13326-019-0215-3)
• Williams, L., Arribas-Ayllon, M., Artemiou, A. and Spasic, I. 2019. Comparing the utility of different classification schemes for emotive language analysis. Journal of Classification 36(3), pp. 619-648. (10.1007/s00357-019-9307-0)
• Chopard, D. and Spasic, I. 2019. A deep learning approach to self-expansion of abbreviations based on morphology and context distance. Presented at: SLSP 2019: 7th International Conference on Statistical Language and Speech Processing, Ljubljana, Slovenia, 14-16 October 2019 Presented at Martín-Vide, C., Purver, M. and Pollak, S. eds.Statistical Language and Speech Processing: 7th International Conference, SLSP 2019, Ljubljana, Slovenia, October 14–16, 2019, Proceedings, Vol. 11816. Springer pp. 71-82., (10.1007/978-3-030-31372-2_6)
• Spasic, I., Owen, D., Knight, D. and Artemiou, A. 2019. Unsupervised multi-word term recognition in Welsh. Presented at: Celtic Language Technology Workshop 2019, Dublin, Ireland, 19 August 2019 Presented at Lynn, T. et al. eds.Proceedings of the Celtic Language Technology Workshop. European Association for Machine Translation
• Dai, X., Spasic, I., Meyer, B., Chapman, S. and Andres, F. 2019. Machine learning on mobile: An on-device inference app for skin cancer detection. Presented at: 2019 4th International Conference on Fog and Mobile Edge Computing, Rome, Italy, 10-13 June 20192019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE pp. 301-305., (10.1109/FMEC.2019.8795362)
• Nieva De La Hidalga, A., Owen, D., Spasic, I., Rosin, P. and Sun, X. 2019. Use of semantic segmentation for increasing the throughput of digitisation workflows for natural history collections. Presented at: Biodiversity_Next 2019, Leiden, The Netherlands, 21-25 October 2019Biodiversity Information Science and Standards, Vol. 3. Vol. e37161. Pensoft, (10.3897/biss.3.37161)

2018

• Hannah, C., Spasic, I. and Corcoran, P. 2018. A computational model of pedestrian road safety: the long way round is the safe way home. Accident Analysis & Prevention 121, pp. 347-357. (10.1016/j.aap.2018.06.004)
• Bannister, C. A., Halcox, J. P., Currie, C. J., Preece, A. and Spasic, I. 2018. A genetic programming approach to development of clinical prediction models: a case study in symptomatic cardiovascular disease. PLoS ONE 13(9), article number: e0202685. (10.1371/journal.pone.0202685)
• Hannah, C., Spasic, I. and Corcoran, P. 2018. Modelling pedestrian safety with respect to road traffic crashes by estimating the safety of paths. Presented at: GIScience Research UK Conference, Leicester, England, 18-20 April 2018.
• Spasic, I., Corcoran, P., Gagarin, A. and Buerki, A. 2018. Head to head: Semantic similarity of multi-word terms. IEEE Access 6, pp. 20545-20557. (10.1109/ACCESS.2018.2826224)
• Spasic, I., Owen, D., Smith, A. and Button, K. 2018. Closing in on open-ended patient questionnaires with text mining. Presented at: UK Healthcare Text Analytics Conference (HealTAC), Manchester, UK, 18-19 April 2018.
• Preece, A., Spasic, I., Evans, K., Rogers, D., Webberley, W., Roberts, C. and Innes, M. 2018. Sentinel: a co-designed platform for semantic enrichment of social media streams. IEEE Transactions on Computational Social Systems 5(1), pp. 118-131. (10.1109/TCSS.2017.2763684)
• Spasic, I. 2018. Acronyms as an integral part of multi–word term recognition - A token of appreciation. IEEE Access 6, pp. 8351-8363. (10.1109/ACCESS.2018.2807122)
• Button, K., Nicholas, K., Busse-Morris, M., Collins, M. and Spasic, I. 2018. Integrating self-management support for knee injuries into routine clinical practice: TRAK intervention design and delivery. Musculoskeletal Science and Practice 33, pp. 53-60. (10.1016/j.msksp.2017.11.002)
• Button, K., Owen, D., Lau, T., Spasic, I., Playle, R., Hannaway, L. and Jones, S. 2018. Improving access to care and treatment for patients with hip and knee pain at the interface between primary and secondary care. Presented at: OARSI 2018 World Congress on Osteoarthritis, Liverpool, UK, 26-29 April 2018.
• Button, K., Nicholas, K., Busse-Morris, M., Collins, M. and Spasic, I. 2018. An evaluation of TRAK physiotherapy self management intervention development and delivery for knee conditions. Presented at: OARSI 2018 World Congress on Osteoarthritis, Liverpool, 26-29 April 2018. pp. -.

2017

• Dunphy, E., Hamilton, F., Spasic, I. and Button, K. 2017. Acceptability of a digital healthintervention alongside physiotherapy to support patients following anterior cruciateligament reconstruction. BMC Musculoskeletal Disorders 18, article number: 471. (10.1186/s12891-017-1846-0)
• Dai, X., Spasic, I. and Andres, F. 2017. A framework for automated rating of online reviews against the underlying topics. Presented at: ACM Southeast Conference, Kennesaw State University, Georgia, USA, 13-15 April 2017ACM SE '17 Proceedings of the SouthEast Conference. New York: ACM pp. 164-167., (10.1145/3077286.3077291)
• Neale, S. et al. 2017. The CorCenCC crowdsourcing app: a bespoke tool for the user-driven creation of the national corpus of contemporary Welsh. Presented at: The 9th International Corpus Linguistics Conference, Birmingham, UK, 24-28 July 2017.

2016

• Button, K. and Spasic, I. 2016. Web based interventions for self-management of rehabilitation. Presented at: Enhanced Recovery after Surgery (ERAS) UK Conference, Cardiff, UK, 4th November.

2015

• Williams, L., Bannister, C., Arribas-Ayllon, M., Preece, A. and Spasic, I. 2015. The role of idioms in sentiment analysis. Expert Systems with Applications 42(21), pp. 7375-7385. (10.1016/j.eswa.2015.05.039)
• Button, K., Roos, P. E., Spasic, I., Adamson, P. and Van Deursen, R. W. M. 2015. The clinical effectiveness of self-care interventions with an exercise component to manage knee conditions: a systematic review. The Knee 22(5), pp. 360-371. (10.1016/j.knee.2015.05.003)
• Moore, S. C. et al. 2015. All-Wales Licensed Premises Intervention (AWLPI): a randomised controlled trial of an intervention to reduce alcohol-related violence. Public Health Research 3(10) (10.3310/phr03100)
• Spasic, I., Zhao, B., Jones, C. B. and Button, K. 2015. KneeTex: An ontology-driven system for information extraction from MRI reports. Journal of Biomedical Semantics 6, article number: 34. (10.1186/s13326-015-0033-1)
• Spasic, I. et al. 2015. TRAK application suite: A web-based intervention for delivering standard care for the rehabilitation of knee conditions. JMIR Research Protocols 4(4), article number: e122. (10.2196/resprot.4091)
• Evans, K. et al. 2015. Dynamically reconfigurable workflows for time-critical applications. Presented at: SC15: The International Conference for High Performance Computing, Networking, Storage, and Analysis, New York City, NY, USA, 16-19 November 2015WORKS '15 Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science. pp. 1-10., (10.1145/2822332.2822339)

2014

• Spasic, I., Livsey, J., Keane, J. A. and Nenadic, G. 2014. Text mining of cancer-related information: review of current status and future directions. International Journal of Medical Informatics 83(9), pp. 605-623. (10.1016/j.ijmedinf.2014.06.009)
• Bhachu, L., Soldatova, L., Spasic, I. and Button, K. 2014. Mobile application KneeCare to support knee rehabilitation. Presented at: Science and Information Conference (SAI), London, UK, 27-29 August 2014.
• Bannister, C. A., Currie, C. J., Preece, A. and Spasic, I. 2014. Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease. Value in Health 17(3), pp. A200-A201. (10.1016/j.jval.2014.03.1171)
• Bannister, C., Poole, C. D., Jenkins-Jones, S., Morgan, C. L., Elwyn, G., Spasic, I. and Currie, C. J. 2014. External validation of the UKPDS risk engine in incident type 2 diabetes: a need for new type 2 diabetes-specific risk equations. Diabetes Care 37(2), pp. 537-545. (10.2337/dc13-1159)
• Moore, S. C. et al. 2014. All-Wales licensed premises intervention (AWLPI): a randomised controlled trial to reduce alcohol-related violence. BMC Public Health 14(1), pp. -., article number: 21. (10.1186/1471-2458-14-21)

2013

• Spasic, I., Greenwood, M., Preece, A. D., Francis, N. A. and Elwyn, G. 2013. FlexiTerm: a flexible term recognition method. Journal of Biomedical Semantics 4, article number: 27. (10.1186/2041-1480-4-27)
• Greenwood, M., Elwyn, G., Francis, N. A., Preece, A. D. and Spasic, I. 2013. Automatic extraction of personal experiences from patients' blogs: A case study in chronic obstructive pulmonary disease. Presented at: Third International Conference on Social Computing and its Applications, Karlsruhe, Germany, 30th September - 2nd October 2013.
• Button, K., Van Deursen, R. W. M., Soldatova, L. and Spasic, I. 2013. TRAK ontology: defining standard care for the rehabilitation of knee conditions. Journal of Biomedical Informatics 46(4), pp. 615-625. (10.1016/j.jbi.2013.04.009)
• Button, K., Roos, P. E., Spasic, I., Pataky, T. and Van Deursen, R. W. M. 2013. Development of iKnee: A web based application using biomechanical data to optomize knee rehabilitation within the home environment. Presented at: The 32nd Scientific Meeting of the Physiotherapy Research Society, Cardiff, UK, 9 April 2013.
• Smallbone, K. et al. 2013. A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes. FEBS Letters 587(17), pp. 2832-2841. (10.1016/j.febslet.2013.06.043)

2012

• Burnap, P., Spasic, I., Gray, W. A., Hilton, J. C., Rana, O. F. and Elwyn, G. 2012. Protecting patient privacy in distributed collaborative healthcare environments by retaining access control of shared information. Presented at: 2012 International Conference on Collaboration Technologies and Systems (CTS), Denver, CO, USA, 21-25 May 2012 Presented at Smari, W. W. and Charles, F. eds.2012 International Conference on Collaboration Technologies and Systems (CTS), Vol. 14. Los Alamitos, CA: IEEE pp. 490-497., (10.1109/CTS.2012.6261095)
• Spasic, I., Burnap, P., Greenwood, M. and Arribas-Ayllon, M. 2012. A naïve Bayes approach to classifying topics in suicide notes. Biomedical Informatics Insights 5(Supp 1), pp. 87-97. (10.4137/BII.S8945)

2010

• Spasic, I., Sarafraz, F., Keane, J. A. and Nenadic, G. 2010. Medication information extraction with linguistic pattern matching and semantic rules. Journal of the American Medical Informatics Association 17(5), pp. 532-535. (10.1136/jamia.2010.003657)
• Dada, J. O., Spasic, I., Paton, N. W. and Mendes, P. 2010. SBRML: a markup language for associating systems biology data with models. Bioinformatics 26(7), pp. 932-938. (10.1093/bioinformatics/btq069)
• Swainston, N., Jameson, D., Li, P., Spasic, I., Mendes, P. and Paton, N. W. 2010. Integrative information management for systems biology. Lecture Notes in Computer Science 6254, pp. 164-178. (10.1007/978-3-642-15120-0_13)
• Masseroli, M., Paton, N. W. and Spasic, I. 2010. Chapter 15: Search Computing and the Life Sciences. Lecture Notes in Computer Science 5950, pp. 291-306. (10.1007/978-3-642-12310-8_15)
• Li, P. et al. 2010. Systematic integration of experimental data and models in systems biology. BMC Bioinformatics 11, article number: 582. (10.1186/1471-2105-11-582)

2009

• Brown, M. et al. 2009. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 134(7), pp. 1322-1332. (10.1039/b901179j)
• Spasic, I., Simeonidis, E., Messiha, H. L., Paton, N. W. and Kell, D. B. 2009. KiPar, a tool for systematic information retrieval regarding parameters for kinetic modelling of yeast metabolic pathways. Bioinformatics 25(11), pp. 1404-1411. (10.1093/bioinformatics/btp175)
• Yang, H., Spasic, I., Keane, J. A. and Nenadic, G. 2009. A Text Mining Approach to the Prediction of Disease Status from Clinical Discharge Summaries. Journal of The American Medical Informatics Association 16(4), pp. 596-600. (10.1197/jamia.M3096)
• Yang, H., Spasic, I., Keane, J. A. and Nenadic, G. 2009. A Text Mining Approach to the Prediction of Disease Status from Clinical Discharge Summaries. JAMIA Journal of the American Medical Informatics Assocation 16(4), pp. 596-600. (10.1197/jamia.M3096)

2008

• Herrgard, M. J. et al. 2008. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnology 26(10), pp. 1155-1160. (10.1038/nbt1492)
• Spasic, I., Schober, D., Sansone, S., Rebholz-Schuhmann, D., Kell, D. B. and Paton, N. W. 2008. Facilitating the development of controlled vocabularies for metabolomics technologies with text mining. BMC Bioinformatics 9(Supp 5), article number: S5. (10.1186/1471-2105-9-S5-S5)
• Dunn, W. B. et al. 2008. A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. International Journal of Epidemiology 37(S1), pp. i23-i30. (10.1093/ije/dym281)

2007

• Dunn, W. B. et al. 2007. Serum metabolomics reveals many novel metabolic markers of heart failure, including pseudouridine and 2-oxoglutarate. Metabolomics 3(4), pp. 413-426. (10.1007/s11306-007-0063-5)
• Sansone, S. et al. 2007. Metabolomics standards initiative: ontology working group work in progress. Metabolomics 3(3), pp. 249-256. (10.1007/s11306-007-0069-z)

2006

• Wilkinson, S. J., Spasic, I. and Ellis, D. I. 2006. Genomes to systems 3. Metabolomics 2(3), pp. 165-170. (10.1007/s11306-006-0030-6)
• Spasic, I. et al. 2006. MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics. BMC Bioinformatics 7, article number: 281. (10.1186/1471-2105-7-281)

2005

• Kell, D. B., Brown, M., Davey, H. M., Dunn, W. B., Spasic, I. and Oliver, S. G. 2005. Metabolic footprinting and systems biology: the medium is the message. Nature Reviews. Microbiology 3(7), pp. 557-565. (10.1038/nrmicro1177)
• Spasic, I., Ananiadou, S. and Tsujii, J. I. 2005. MaSTerClass: a case-based reasoning system for the classification of biomedical terms. Bioinformatics 21(11), pp. 2748-2758. (10.1093/bioinformatics/bti338)
• Brown, M. et al. 2005. A metabolome pipeline: from concept to data to knowledge. Metabolomics 1(1), pp. 39-51. (10.1007/s11306-005-1106-4)
• Spasic, I., Ananiadou, S., McNaught, J. and Kumar, A. 2005. Text mining and ontologies in biomedicine: making sense of raw text. Briefings in Bioinformatics 6(3), pp. 239-251. (10.1093/bib/6.3.239)
• Spasic, I. and Ananiadou, S. 2005. A flexible measure of contextual similarity for biomedical terms. Pacific Symposium on Biocomputing 10, pp. 197-208.

2004

• Spasic, I. and Ananiadou, S. 2004. Using automatically learnt verb selectional preferences for classification of biomedical terms. Journal of Biomedical Informatics 37(6), pp. 483-497. (10.1016/j.jbi.2004.08.002)
• Nenadic, G., Spasic, I. and Ananiadou, S. 2004. Mining term similarities from corpora. Terminology 10(1), pp. 55-80. (10.1075/term.10.1.04nen)

2003

• Nenadic, G., Spasic, I. and Ananiadou, S. 2003. Terminology-driven mining of biomedical literature. Bioinformatics 19(8), pp. 938-943. (10.1093/bioinformatics/btg105)

2002

• Nenadic, G., Mima, H., Spasic, I., Ananiadou, S. and Tsujii, J. 2002. Terminology-driven literature mining and knowledge acquisition in biomedicine. International Journal of Medical Informatics 67(1-3), pp. 33-48. (10.1016/S1386-5056(02)00055-2 |)