Yr Athro Irena Spasic

2020

• 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), pp. 189-199. (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.et al. 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)
• 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. -.
• Dai, X.et al. 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. -.

2019

• Spasic, I.et al. 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.et al. 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)
• Spasic, I.et al. 2019. Cohort selection from longitudinal patient records: Text mining approach. JMIR Medical Informatics 7(4), article number: e15980. (10.2196/15980)
• 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.et al. 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.et al. 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.et al. 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 pp. -., (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.et al. 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.et al. 2018. Head to head: Semantic similarity of multi-word terms. IEEE Access 6, pp. 20545-20557. (10.1109/ACCESS.2018.2826224)
• Spasic, I.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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)
• 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)
• 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)

2014

• Spasic, I.et al. 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.et al. 2014. Mobile application KneeCare to support knee rehabilitation. Presented at: Science and Information Conference (SAI), London, UK, 27-29 August 2014.
• Bannister, C. A.et al. 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.et al. 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.et al. 2013. FlexiTerm: a flexible term recognition method. Journal of Biomedical Semantics 4, article number: 27. (10.1186/2041-1480-4-27)
• Greenwood, M.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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.et al. 2010. SBRML: a markup language for associating systems biology data with models. Bioinformatics 26(7), pp. 932-938. (10.1093/bioinformatics/btq069)
• Swainston, N.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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.et al. 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 |)