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Aldraimli, M. 2023. Predictive Modelling Approach to Data-Driven Computational Preventive Medicine. PhD thesis University of Westminster Computer Science and Engineering https://doi.org/10.34737/w4ww2

Banziger, R.B., Basukoski, A. and Chaussalet, T.J. 2023. Discovering Process Models from Patient Notes. 23rd International Conference on Computational Science (ICCS-23). Prague 03 - 05 Jul 2023 Springer. https://doi.org/10.1007/978-3-031-36024-4_18

Vali, M., Salimifard, K., Gandomi, A.H. and Chaussalet, T.J. 2022. Care process optimization in a cardiovascular hospital: an integration of simulation–optimization and data mining. Annals of Operations Research. 318, pp. 685-712. https://doi.org/10.1007/s10479-022-04831-z

Dilna, K.T., Anitha, J., Angelopoulou, A., Chaussalet, T.J., Kapetanios, D.E. and Jude Hemanth, D. 2022. Classification of Uterine Fibroids in Ultrasound Images Using Deep Learning Model. International Conference on Computational Science ICCS 2022. London, UK 21 - 23 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-08757-8_5

Smera Premkumar, K., Angelopoulou, A., Chaussalet, T.J., Kapetanios, D.E. and Jude Hemanth, D. 2022. Super-Resolution Convolutional Network for Image Quality Enhancement in Remote Photoplethysmography based Heart Rate Estimation. International Conference on Computational Science ICCS 2022. London, UK 21 - 23 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-08757-8_15

Nazyrova, N., Chaussalet, T.J. and Chahed, S. 2022. Machine Learning models for predicting 30-day readmission of elderly patients using custom target encoding approach. International Conference on Computational Science ICCS 2022. London, UK 21 - 23 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-08757-8_12

Surendran, R., Anitha, J., Angelopoulou, A., Chaussalet, T.J., Kapetanios, D.E. and Jude Hemanth, D. 2022. Transfer Learning based Natural Scene Classification for Scene Understanding by Intelligent Machines. International Conference on Computational Science ICCS 2022. London, UK 21 - 23 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-08754-7_6

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

Ogunbiyi, O., Basukoski, A. and Chaussalet, T.J. 2022. Comparative analysis of clustering-based remaining-time predictive process monitoring approaches. International Journal of Business Process Integration and Management. 10 (3/4), pp. 230-241. https://doi.org/10.1504/IJBPIM.2021.124023

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

Ogunbiyi, O., Basukoski, A. and Chaussalet, T.J. 2021. An Exploration of Ethical Decision Making with Intelligence Augmentation. Social Sciences. 10 (2) 57. https://doi.org/10.3390/socsci10020057

Ogunbiyi, O., Basukoski, A. and Chaussalet, T.J. 2020. Investigating the Diffusion of Workload-Induced Stress—A Simulation Approach. Information. 12 (1) 11. https://doi.org/10.3390/info12010011

Bolotov, A. Bolotov, A. (ed.) 2020. Proceedings of the 4th Student-STAFF Research Conference 2020 School of Computer Science and Engineering SSRC2020. University of Westminster.

Ogunbiyi, N., Basukoski, A. and Chaussalet, T.J. 2020. Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach. Algorithms. 13 (11), p. e267. https://doi.org/10.3390/a13110267

Aldraimli, M., Soria, D., Parkinson, J., Thomas, E.L., Bell, J.D., Dwek, M. and Chaussalet, T.J. 2020. Machine Learning Prediction of Susceptibility to Visceral Fat Associated Diseases. Health and Technology. 10, pp. 925-944. https://doi.org/10.1007/s12553-020-00446-1

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

Aldraimli, M., Soria, D., Parkinson, J., Whitcher, B., Thomas, E.L., Bell, J.D., Chaussalet, T.J. and Dwek, M. 2019. Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases. MEDICON 2019: XV Mediterranean Conference on Medical and Biological Engineering and Computing. Coimbra, Portugal 26 - 28 Sep 2019 Springer. https://doi.org/10.1007/978-3-030-31635-8_81

He, F., Chaussalet, T.J. and Qu, R. 2019. Controlling Understaffing with Conditional Value-at-Risk Constraint for an Integrated Nurse Scheduling Problem under Patient Demand Uncertainty. Operations Research Perspectives. 6 (2019), p. 100119 100119. https://doi.org/10.1016/j.orp.2019.100119

He, F., Chaussalet, T.J. and Qu, R. 2019. Modelling the Home Health Care Nurse Scheduling Problem for Patients with Long-Term Conditions in the UK. 33rd International ECMS Conference on modelling and Simulation. Universita degli Studi della Campania, Caserta, Area of Napoli, Italy 11 - 14 Jun 2019 European Council for Modeling and Simulation. https://doi.org/10.7148/2019-0317

Zebin, T. and Chaussalet, T.J. 2019. Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health 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.2019.8791466