Search WestminsterResearch

Virtue, A., Chaussalet, T.J. and Kelly, J. 2011. A case study using simplified discrete-event simulation models as a tool to reconfigure health care services. in: Operational Research Information National Health Policy: proceedings of the 37th ORAHS conference School of Mathematics, Cardiff University.

Chaussalet, T.J., Xie, H. and Millard, P.H. 2006. A closed queueing network approach to the analysis of patient flow in health care systems. Methods of Information in Medicine. 45 (5), pp. 492-497.

Aldraimli, M., Nazyrova, N., Djumanov, A., Sobirov, I. and Chaussalet, T.J. 2022. A Comparative Machine Learning Modelling Approach for Patients' Mortality Prediction in Hospital Intensive Care Unit . Sotirov, S.S., Pencheva, T., Kacprzyk, J., Atanassov, K.T., Sotirova, E. and Staneva, G. (ed.) International Symposium on Bioinformatics and Biomedicine. Burgas, Bulgaria 08 - 10 Oct 2020 Springer. https://doi.org/10.1007/978-3-030-96638-6_2

Kandasamy, Nichalini, Chaussalet, Thierry and Basukoski, Artie 2023. A Conceptual Framework to Predict Disease Progressions in Patients with Chronic Kidney Disease, Using Machine Learning and Process Mining. in: Healthcare Transformation with Informatics and Artificial Intelligence IOS Press. pp. 190-193

Pandey, Sanjib Raj, Smith, Alan, Gall, Edmund Nigel, Bhatnagar, Ajay and Chaussalet, Thierry 2022. A Conceptual Framework to Predict Mental Health Patients' Zoning Classification. Studies in Health Technology and Informatics. 289, pp. 321-324. https://doi.org/10.3233/SHTI210924

Xie, H., Chaussalet, T.J. and Millard, P.H. 2005. A continuous time Markov model for the length of stay of elderly people in institutional long-term care. Journal of the Royal Statistical Society: Series A. 168 (1), pp. 51-61. https://doi.org/10.1111/j.1467-985X.2004.00335.x

Aldraimli, M., Soria, D., Grishchuck, D., Ingram, S., Lyon, R., Mistry, A., Oliveira, J., Samuel, R., Shelley, L.E.A., Osman, S., Dwek, M., Azria, D., Chang-Claude, J., Gutiérrez-Enríquez, S., De Santis, M.C., Rosenstein, B.S., De Ruysscher, D., Sperk, E., Symonds, R.P., Stobart, H., Vega, A., Veldeman, L., Webb, A, Christopher, J.T., West, C.M., Rattay, T., REQUITE consortium and Chaussalet, T.J. 2021. A Data Science Approach for Early-Stage Prediction of Patient’s Susceptibility to Acute Side Effects of Advanced Radiotherapy. Computers in Biology and Medicine. 135 104624. https://doi.org/10.1016/j.compbiomed.2021.104624

Vasilakis, C., El-Darzi, E. and Chountas, P. 2004. A data warehouse environment for storing and analyzing simulation output data. in: Ingalls, R.G., Rossetti, M.D., Smith, J.S. and Peters, B.A. (ed.) Proceedings of the 2004 Winter Simulation Conference USA IEEE .

Demir, E., Chahed, S., Chaussalet, T.J., Toffa, S.E. and Fouladinajed, F. 2012. A decision support tool for health service re-design. Journal of Medical Systems. 36 (2), pp. 621-630. https://doi.org/10.1007/s10916-010-9526-8

Zebin, T., Rezvy, S. and Chaussalet, T.J. 2019. A deep learning approach for length of stay prediction in clinical settings from medical records. 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology. Certosa di Pontignano, Siena - Tuscany, Italy 09 - 11 Jul 2019 IEEE . https://doi.org/10.1109/CIBCB.2019.8791477

Pelletier, C., Chaussalet, T.J. and Xie, H. 2005. A framework for predicting gross institutional long-term care cost arising from known commitments at local authority level. Journal of the Operational Research Society. 56 (2), pp. 144-152. https://doi.org/10.1057/palgrave.jors.2601892

Chishti, I., Chaussalet, T.J. and Basukoski, A. 2016. A general framework for Business Process Modelling (BPM) based on Formal Temporal Theory with an application to Hospital Patient flows. 8th IMA International Conference on Quantitative Modelling in the Management of Health and Social Care. Asia House, London 21 - 23 Mar 2016 Institute of Mathematics and its Applications.

Demir, E., Chaussalet, T.J., Weingarten, N. and Kiss, T. 2009. A Grid implementation for profiling hospitals based on patient readmissions. in: McClean, S.I., Millard, P.H., El-Darzi, E. and Nugent, C. (ed.) Intelligent patient management Springer.

Virtue, A. 2013. A healthcare space planning simulation model for Accident and Emergency (A&E). PhD thesis University of Westminster School of Electronics and Computer Science https://doi.org/10.34737/8z1y4

Asaduzzaman, M., Chaussalet, T.J. and Robertson, N.J. 2010. A loss network model with overflow for capacity planning of a neonatal unit. Annals of Operations Research. 178 (1), pp. 67-76. https://doi.org/10.1007/s10479-009-0548-x

Demir, E., Chaussalet, T.J., Xie, H. and Millard, P.H. 2006. A method for determining an emergency readmission time window for better patient management. in: Lee, D.J., Nutter, B., Antani, S., Mitra, S. and Archibald, J. (ed.) Nineteenth IEEE International Symposium on Computer-Based Medical Systems: 22-23 June June 2006, Salt Lake City, Utah. Proceedings Las Alamitos, USA IEEE . pp. 789-793

Xie, H., Chaussalet, T.J. and Millard, P.H. 2006. A model-based approach to the analysis of patterns of length of stay in institutional long-term care. IEEE Transactions on Information Technology in Biomedicine. 10 (3), pp. 512-518. https://doi.org/10.1109/TITB.2005.863820

Rathi, M. 2015. A novel framework for predicting patients at risk of readmission. PhD thesis University of Westminster Department of Computer Science https://doi.org/10.34737/9x22z

Chishti, I. 2019. A Process Modelling Framework Based on Point Interval Temporal Logic with an Application to Modelling Patient Flows. PhD thesis University of Westminster School of Computer Science and Engineering https://doi.org/10.34737/qy760

Adeyemi, S. and Chaussalet, T.J. 2008. A random effects sensitivity analysis for patient pathways model. in: Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, IEEE CBMS 2008, Jyväskylä, 17-19 June 2008 Los Alamitos, USA IEEE . pp. 536-538