Kyrimi, E., McLachlan, S., Dube, K., Neves, M. R., Fahmi, A., & Fenton, N. (2020). A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future. Manuscript accepted for publication in the journal AI in Medicine. arXiv preprint https://arxiv.org/abs/2002.08627.
Daley, B., Kyrimi, E., Dube, K., Fenton, N., Hitman, G., & McLachlan, S. (2021). The Absence of Data Visualisation in Midwifery. Manuscript submitted to the IEEE International Conference on Health Informatics (ICHI’21).
McLachlan, S., Kyrimi, E., Dube, K., Fenton, N., & Webley, L. (2021). Lawmaps: Enabling Legal AI development through Visualisation of the Implicit Structure of Legislation and Lawyerly Process. Manuscript in review with the Journal of Artificial Intelligence in Law. https://arxiv.org/pdf/2011.00586.
Hartmann M, Fenton NE and Dobson R. (2021) Recognizing and Adjusting for Paradoxes in Multiple Sclerosis Datasets Using Bayesian Networks. Manuscript submitted to the IEEE International Conference on Health Informatics (ICHI’21)
Fenton, N. E., McLachlan, S., Lucas, P., Dube, K., Hitman, G., Osman, M., Kyrimi, E. Neil, M. (2021). A Bayesian network model for personalised COVID19 risk assessment and contact tracing. https://doi.org/10.1101/2020.07.15.20154286
Fenton, N. E. (2020) How to explain an increasing proportion of people testing positive for COVID if there is neither an increase in proportion of genuine cases nor increase in the false positive rate. https://doi.org/10.13140/RG.2.2.27902.20806
Collins, R., & Fenton, N. (2020). Bayesian network modelling for early diagnosis and prediction of Endometriosis. MedRxiv, 2020.11.04.20225946. https://doi.org/10.1101/2020.11.04.20225946
Butcher, R., & Fenton, N. E. (2020). Extending the range of symptoms in a Bayesian Network for the Predictive Diagnosis of COVID-19, medRxiv https://doi.org/10.1101/2020.10.22.20217554
Prodhan, G., & Fenton, N. E. (2020). Extending the range of COVID-19 risk factors in a Bayesian network model for personalised risk assessment. medRxiv https://doi.org/10.1101/2020.10.20.20215814
Fenton N. E, Neil M, McLachlan S, Osman M (2020), Misinterpreting statistical anomalies and risk assessment when analysing Covid-19 deaths by ethnicity. Manuscript accepted for publication in Significance. 10.13140/RG.2.2.18957.56807
Fenton, N E. (2020). A Note on UK Covid19 death rates by religion: which groups are most at risk? http://arxiv.org/abs/2007.07083
McLachlan, S., Dube, K., Puybareau, C., Pitard, M., Buchanan, D., Chiketero, P., Gallagher, T., Daley, B., Hitman, G., & Fenton, N. (2021). Realistic synthetic health condition timelines: generating the patient history using contextually appropriate disease burden and health statistics. Manuscript submitted to the IEEE International Conference on Health Informatics (ICHI’21), Easychair Preprint: https://easychair.org/publications/preprint_open/BhHm
Neves, M., Daley, B., Hitman, G., Huda, M., McLachlan, S., Finer, S., & Marsh, W. (2021). Causal Dynamic Bayesian Networks for the management of Glucose Control in Gestational Diabetes. Manuscript submitted to the IEEE International Conference on Health Informatics (ICHI’21).
Fenton, N., McLachlan, S., Lucas, P., Dube, K., Hitman, G., Osman, M., Kyrimi, E., Neil, M. (2020). A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing. MedRxiv, 2020.07.15.20154286. https://www.medrxiv.org/content/10.1101/2020.07.15.20154286v2
McLachlan, S., Lucas, P., Dube, K., Hitman, G., Osman, M., Kyrimi, E., Neil, M., & Fenton, N. (2020). Bluetooth smartphone apps: Are they the most private and effective solution for COVID-19 contact tracing?. ArXiv Preprint: https://arxiv.org/abs/2005.06621
McLachlan, S., Lucas, P., Dube, K., Hitman, G., Osman, M., Kyrimi, E., Neil, M., & Fenton, N. (2020). The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach. http://dx.doi.org/10.13140/RG.2.2.27042.66243
Fenton, N., Hitman, G., Neil, M., Osman, M., & McLachlan, S. (2020). Causal explanations, error rates, and human judgement biases missing from the COVID-19 narrative and statistics. Manuscript submitted to BMC Public Health. Preprint available from: https://doi.org/10.31234/osf.io/p39a4
Wilson, W., McLachlan, S., Dube, K. & Jayamaha, N. (2020) Complex Quality Improvement Networks: Government responses to COVID-19 Modelled as Complex Adaptive System behaviour. https://easychair.org/publications/preprint_open/TqQL
McLachlan, S., Kyrimi, E., & Fenton, N.E. (2019). Public Authorities as Defendant: Using Bayesian Networks to determine the Likelihood of Success for Negligence claims in the wake of Oakden. Preprint available: https://arxiv.org/abs/2002.05664
Kyrimi, E., McLachlan, S., Dube, K., & Fenton, N. (2019). Bayesian Networks on Healthcare: the chasm between research enthusiasm and clinical adoption. MedRxiv preprint https://www.medrxiv.org/content/10.1101/2020.06.04.20122911v1.
