Publications: Medical and health risk
For full list of my publications relating to Covid-19 see here.
Neil, M., McLachlan, S. and Fenton, N. (2024) ‘The extent and impact of vaccine status miscategorisation on covid-19 vaccine efficacy studies’. medRxiv, https://doi.org/10.1101/2024.03.09.24304015
McLachlan, S. Daley, B., Kyrimi, E., Dube, K., Saidi, S., Grosan, C., Neil, M., Fenton, N., Rose, L. (2023). Approach and method for Bayesian Network Modelling: The case for pregnancy outcomes in England and Wales. 17th International Conference on Health Informatics (HEALTHINF). Rome, Italy. February 2024. https://doi.org/10.5220/0012428600003657
Gerry A Quinn, Michael Connolly, Norman E. Fenton, Steven J. Hatfill, Paul Hynds, Coilín ÓhAiseadha, Karol Sikora, Willie Soon, Ronan Connolly (2024), "Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe", Journal of Clinical Medicine, J. Clin. Med. 2024, 13(2), 334; https://doi.org/10.3390/jcm13020334
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Bate St Cliere, A and Fenton N.E. (2024) "Bayesian Network Modelling for the Clinical Diagnosis of Alzheimer’s Disease" https://www.medrxiv.org/content/10.1101/2023.12.30.23300452v1 Online tool to access model via questionnaire interface: https://ad-diagnostic-tool.public.agenaai.app/
Hunte, J. L., Neil, M. & Fenton, N. E. (2024), "A hybrid Bayesian network for medical device risk assessment and management". Reliab. Eng. Syst. Saf. 241, 109630 https://doi.org/10.1016/j.ress.2023.109630
P. Chaichanavichkij, M. Hartmann,S. M. Scott, N. Fenton,C. H. Knowles (2023), "Evaluating the risk factors for the development of benign disorders of defaecation: a surgical perspective", Techniques in Coloproctology, https://doi.org/10.1007/s10151-023-02843-w
McLachlan S, Daley B J, Dube K, Kyrimi E, Neil M, Fenton N E, (2023) "The Health Condition Timeline as a Model for Pregnancy Disease Management", https://doi.org/10.1101/2023.02.06.23285418
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McLachlan S, Neil M, Choi Y, Craig S, Dube K, Engler J, Osman M, Fenton N E, (2023) "Extended: Analysis of COVID-19 Vaccine Death Reports from the Vaccine Adverse Events Reporting System (VAERS) Database", http://dx.doi.org/10.13140/RG.2.2.23499.87842
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Fenton NE, Neil M (2023), "The Lancet and the Pfizer Vaccine: A Case Study in Academic Censorship and Deceit in the Covid Era", http://dx.doi.org/10.13140/RG.2.2.29792.56321
MacBrayne A, Soyel H, Marsh W, Fenton N E, Pitzalis C, Curzon P, and Humby F (2023), "Attitudes to Technology supported Rheumatoid Arthritis care: Opportunities & Barriers for technology in RA - Key themes from Qualitative arm of Mixed-Methods Study", British Journal of Rheumatology 62 (Supplement_2) http://dx.doi.org/10.1093/rheumatology/kead104.174
MacBrayne A, Soyel H, Marsh W, Fenton N E, Pitzalis C, Curzon P, and Humby F (2023), "Attitudes to Technology supported Rheumatoid Arthritis care Questionnaire study: Barriers to People with RA & their clinicians using Technology in the care pathway", British Journal of Rheumatology 62 (Supplement_2) http://dx.doi.org/10.1093/rheumatology/kead104.154
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MacBrayne A, Soyel H, Marsh W, Fenton N E, Pitzalis C, Curzon P, and Humby F (2023), "Attitudes to technology-supported rheumatoid arthritis care, questionnaire study: opportunities for technology to improve RA care" British Journal of Rheumatology 62 (Supplement_2), http://dx.doi.org/10.1093/rheumatology/kead104.322
Fenton, N E (2023) 'Scottish cardiac ambulance data shows worrying increase in incidents' UK Column, 3 Jan 2023, https://www.ukcolumn.org/article/scottish-cardiac-ambulance-data-shows-worrying-increase-in-incidents
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Hartmann, M., Fenton, N E, Dobson, R. (2022). Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis. In: , et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science, vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_2
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Fenton, N. E., Neil, M., Craig, C. & McLachlan, S. (2022). "What the ONS Mortality Covid-19 Surveillance Data can tell us about Vaccine Safety and Efficacy", http://dx.doi.org/10.13140/RG.2.2.30898.07362
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Fenton, N. E., (2022) The Bangladesh Mask study: a Bayesian perspective, http://dx.doi.org/10.13140/RG.2.2.26189.92649
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Craig, C., Neil, M., Fenton, N E., McLachlan, S., Smalley, J., Guetzkow, J., Engler, J.,Rusell, D., Rose, J. (2022). Official mortality data for England reveal systematic undercounting of deaths occurring within first two weeks of Covid-19 vaccination. https://doi.org/http://dx.doi.org/10.13140/RG.2.2.12472.42248
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Chaichanavichkij, P., Hartmann, M., McLachlan, S., Scott, S., Fenton, N. E, & Knowles, C. (2022). O039 Evaluating the influence of risk factors on the development of defaecatory problems: a Delphi study. British Journal of Surgery, 109(Supplement_4). https://doi.org/10.1093/bjs/znac242.039
Martin Neil, Norman Fenton, Joel Smalley, Clare Craig, Joshua Guetzkow, Scott McLachlan, Jonathan Engler, Dan Russell and Jessica Rose (2021), “Official mortality data for England suggest systematic miscategorisation of vaccine status and uncertain effectiveness of Covid-19 vaccination”, http://dx.doi.org/10.13140/RG.2.2.28055.09124 (this is a significantly revised version of http://dx.doi.org/10.13140/RG.2.2.14176.20483)
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Martin Neil, Norman Fenton and Scott McLachlan (2021), "Discrepancies and inconsistencies in UK Government datasets compromise accuracy of mortality rate comparisons between vaccinated and unvaccinated", http://dx.doi.org/10.13140/RG.2.2.32817.10086 [A significantly updated version of the paper is here]
Daley B.J., Ni’Man M., Raniere Neves M., Huda M.S.B., Marsh W, Fenton N. E., Hitman G. A., and McLachlan S (2021) "mHealth Apps for Gestational Diabetes Mellitus that provide Clinical Decision Support or Artificial Intelligence: A Scoping Review", Diabetic Medicine http://dx.doi.org/10.1111/dme.14735
Hartmann M, Fenton NE and Dobson R (2021), "Using Bayesian networks to understand multiple sclerosis risk factor interactions", 37th Congress of the European Committee for Treatment and Research in Multiple Sclerosis, Oct 2021
Fenton, N. E., Neil, M., & McLachlan, G. S. (2021). Paradoxes in the reporting of Covid19 vaccine effectiveness
Why current studies (for or against vaccination) cannot be trusted and what we can do about it,
http://dx.doi.org/10.13140/RG.2.2.32655.30886
Neil, M and Fenton N E (2021), "Bayesian Hypothesis Testing and Hierarchical Modeling of Ivermectin Effectiveness", American Journal of Therapeutics, Vol 28, e577–e579, http://dx.doi.org/10.1097/MJT.0000000000001450
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Neil, M and Fenton N E (2021), "Bayesian hypothesis testing and hierarchical modelling of ivermectin effectiveness in treating Covid-19" http://dx.doi.org/10.13140/RG.2.2.19703.75680
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McLachlan, S, Osman, M, Dube, K, Chiketero, P, Choi, Y, and Fenton N (2021) "Analysis of COVID-19 vaccine death reports from the Vaccine Adverse Events Reporting System (VAERS) Database", http://dx.doi.org/10.13140/RG.2.2.26987.26402
Kyrimi, E., McLachlan, S., Dube, K., Neves, M. R., Fahmi, A., & Fenton, N. (2021). A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future. Artificial Intelligence in Medicine, 117, 102108. https://doi.org/10.1016/J.ARTMED.2021.102108
Fenton, N. E., Neil, M., & McLachlan, G. S. (2021). What proportion of people with COVID-19 do not get symptoms? https://doi.org/10.13140/RG.2.2.33939.60968
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​Kyrimi E, Dube K, Fenton N E, Fahmi A, Raniere Neves M, Marsh W & McLachlan, G. S. (2021), "Bayesian Networks in Healthcare: What is preventing their adoption?" Artificial Intelligence in Medicine, Vol 116, 102079, https://doi.org/10.1016/j.artmed.2021.102079. PrePrint available here
Hartmann M, Fenton NE and Dobson R, “Current Review and Next Steps for Artificial Intelligence in Multiple Sclerosis Risk Research” (2021) Comput. Biol. Med. https://doi.org/10.1016/j.compbiomed.2021.104337 (also available here: Pre-print pdf)
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M. Hartmann, N. Fenton and R. Dobson. “A statistical analysis of multiple sclerosis risk factor interaction with Bayesian networks,” Computational Intelligence Methods for Bioinformatics and Biostatistics, 2021.
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M. Hartmann, N. Fenton and R. Dobson. “Using Bayesian networks to understand multiple sclerosis risk factor interactions,” 37th Congress of the European Committee for Treatment and Research in Multiple Sclerosis, 2021
https://journals.sagepub.com/doi/full/10.1177/13524585211044667
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
McLachlan S, Kyrimi E, Dube K, Hitman G, Simmonds J and Fenton N E, “Towards Standardisation of Evidence-Based Clinical Care Process Specifications” (2020) 26 Health Informatics J. 25 (4), 2512-2538, https://doi.org/10.1177/1460458220906069
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
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. (2020). A Note on UK Covid19 death rates by religion: which groups are most at risk? http://arxiv.org/abs/2007.07083
Fenton, N. E., 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://doi.org/10.1101/2020.07.15.20154286
Kyrimi, E., Neves, M., Neil, M., Marsh, W., McLachlan, S., & Fenton, N. E. (2020). "Medical idioms for clinical Bayesian network development". Journal of Biomedical Informatics, Vol 108, 103495, https://doi.org/10.1016/j.jbi.2020.103495. Accepted version available here
Neil, M., Fenton, N E., Osman, M., & McLachlan, S. (2020). "Coronavirus: our study suggests more people have had it than previously estimated", The Conversation, 26 June 2020
Neil, M., Fenton, N.E, Osman, M., & McLachlan, S. (2020). "Bayesian Network Analysis of Covid-19 data reveals higher Infection Prevalence Rates and lower Fatality Rates than widely reported". Journal of Risk Research, 23 (7-8), 866-879 https://doi.org/10.1080/13669877.2020.1778771 . Preprint: MedRxiv, 2020.05.25.20112466. https://doi.org/10.1101/2020.05.25.20112466 Blog post here
Osman, M., Fenton, N. E. , McLachlan, S., Lucas, P., Dube, K., Hitman, G. A., Kyrimi, E, Neil, M, (2020)."The thorny problems of Covid-19 Contact Tracing Apps: The need for a holistic approach", Journal of Behavioral Economics for Policy, Vol. 4, 57-61. Published version. Also available here.
McLachlan, S., Dube, K., Hitman, G. A., Fenton, N. E., & Kyrimi, E. (2020). Bayesian networks in healthcare: Distribution by medical condition. Artificial Intelligence in Medicine, 107, 101912. https://doi.org/10.1016/J.ARTMED.2020.101912
Dube, K., Mclachlan, S., Zanamwe, N., Kyrimi, E., Thomson, J. S., & Fenton, N. E (2020.). "Managing Knowledge in Computational Models for Global Food, Nutrition and Health Technologies." 2020 IEEE Global Humanitarian Technology Conference (GHTC) (GHTC 2020) https://doi.org/10.1109/GHTC46280.2020.9342880
McLachlan, S., Lucas, P., Dube, K., McLachlan, G. S., Hitman, G. A., Osman, M., Kyrimi, E, Neil, M, Fenton, N. E. (2020). "COVID-19 and contact tracing: literature review and additional analysis", submitted to BMC Public Health
Fenton, N E (2020), "Why most studies into COVID19 risk factors may be producing flawed conclusions-and how to fix the problem", http://arxiv.org/abs/2005.08608 Blog post here
McLachlan, S., Lucas, P., Dube, K., McLachlan, G. S., Hitman, G. A., Osman, M., Kyrimi, E, Neil, M, Fenton, N. E. (2020). "The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach". https://doi.org/10.13140/RG.2.2.27042.66243
McLachlan, S., Lucas, P., Dube, K., Hitman, G. A., Osman, M., Kyrimi, E., … Fenton, N. E. (2020). Bluetooth Smartphone Apps: Are they the most private and effective solution for COVID-19 contact tracing? http://arxiv.org/abs/2005.06621
Fenton, N. E., Neil, M., Osman, M., & McLachlan, S. (2020). "COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing". Journal of Risk Research, 1–4. https://doi.org/10.1080/13669877.2020.1756381
Fenton, N.E., Hitman, G. A., Neil, M., Osman, M., & McLachlan, S. (2020). Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics. PsyArXiv Preprints. https://doi.org/10.31234/OSF.IO/P39A4
Fenton, N. E., Osman, M., Neil, M., & McLachlan, S. (2020). Coronavirus: country comparisons are pointless unless we account for these biases in testing. The Conversation, April 2, 2020 Spanish version: Coronavirus: las comparaciones entre países no tienen sentido a menos que tengamos en cuenta los sesgos en las pruebas.
Fenton, N. E., Osman M, Neil, M., & McLachlan, S. (2020). Improving the statistics and analysis of coronavirus by avoiding bias in testing and incorporating causal explanations for the data. pdf
Kyrimi, E., McLachlan, S., Dube, K., & Fenton, N.E (2020). Bayesian Networks in Healthcare: the chasm between research enthusiasm and clinical adoption. MedRxiv, 2020.06.04.20122911. https://doi.org/10.1101/2020.06.04.20122911
McLachlan S., Kyrimi E., Dube K., Fenton N. (2020) Standardising Clinical Caremaps: Model, Method and Graphical Notation for Caremap Specification. In: Roque A. et al. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2019. Communications in Computer and Information Science, vol 1211. Springer, Cham https://doi.org/10.1007/978-3-030-46970-2_21
Daley, B. J., Kyrimi, E., Dube, K., Fenton, N. E., Hitman, G. A., & McLachlan, S. (2020). Data Visualisation in Midwifery: The Challenge of Seeing what Datasets Hide. Studies in Health Technology and Informatics, 270, 1239–1240. https://doi.org/10.3233/SHTI200381
Mclachlan, S., Dube, K., Kyrimi, E., & Fenton, N. (2019). "LAGOS: learning health systems and how they can integrate with patient care". BMJ Health Care Inform, 26, 100037. https://doi.org/10.1136/bmjhci-2019-100037
Dube, K., McLachlan, S., Zanamwe, N., Kyrimi, E., Thomson, J., & Fenton, N. (2019). "Managing Knowledge Incorporated into Solution Models for Customisable Global Health Technologies". IEEE Global Humanitarian Technology Conference (GHTC), ISBN: 978-1-7281-1780-5/19 pages 303-310.
Zhang, H., Marsh, W. R., Fenton, N., & NEIL, M. (2019). "Realising the Potential for ML from Electronic Health Records". Proc. 1st International ‘Alan Turing’ Conference on Decision Support and Recommender Systems (DSRS-Turing 2019). London, UK. Accepted version (pdf)
McLachlan, Scott, Kudakwashe Dube, Thomas Gallagher, Jennifer A. Simmonds, and Norman Fenton. 2019. “Realistic Synthetic Data Generation: The ATEN Framework.” In , 497–523. Springer, Cham. https://doi.org/10.1007/978-3-030-29196-9_25
Daley, B., Hitman, G., Fenton, N.E., & McLachlan, S. (2019). "Assessment of the methodological quality of local clinical practice guidelines on the identification and management of gestational diabetes". BMJ Open, 9(6), e027285. https://doi.org/10.1136/bmjopen-2018-027285. Full paper (pdf)
McLachlan, S., Dube, K., Johnson, O., Buchanan, D., Potts, H. W. W., Gallagher, T., Marsh, D.W., Fenton, N. E. (2019). "A Framework for Analysing Learning Health Systems: Are we removing the most impactful barriers?", Learning Health Systems, March 2019, Vol 3 (4), e10189 10.1002/lrh2.10189.
McLachlan, S., Kyrimi, E., Dube, K., & Fenton, N. E.. (2019). "Clinical Caremap Development: How can caremaps standardise care when they are not standardised?" In HealthInf 13 Annual International Conference on Health Informatcis. Prague, Czech Republic. Feb 2019 Pre-publication version (pdf)
Fenton, N. E., (2018) "A Bayesian Network and Influence Diagram for a simple example of Drug Economics Decision Making", https://doi.org/10.13140/RG.2.2.33659.77600
McLachlan, S., Potts, H., Dube, K., Buchanan, D., Lean, S., Gallagher, T., Johnson, O., Daley, B., Marsh, W., & Fenton N.E. (2018), "The Heimdall Framework for Supporting Characterisation of Learning Health Systems", BCS Journal of Innovation in Health Informatics, 25(2):77–87, http://dx.doi.org/10.14236/jhi.v25i2.996
McLachlan, S., Dube, K., Buchanan, D., Lean, S., Johnson, O., Potts, H., Gallagher, T,. Marsh, W., Fenton N.E. (2018). "Learning health systems: The research community awareness challenge". Journal of Innovation in Health Informatics, 25(1), 038-040 http://doi.org/10.14236/jhi.v25i1.981
Constantinou, A. C., Fenton, N., Marsh, W., & Radlinski, L. (2016). "From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support", Artificial Intelligence in Medicine, 2016. Vol 67 pages 75-93. http://dx.doi.org/10.1016/j.artmed.2016.01.002, Pre-publication version here.
Constantinou, A. C., Yet, B., Fenton, N., Neil, M., & Marsh, W. (2016). Value of Information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences. Artificial Intelligence in Medicine. 66, pp 41-52 doi:10.1016/j.artmed.2015.09.002 Pre-publication version here.
Fenton NE, Neil M (2015), "Book Review: Malcom Kendrick “Doctoring Data: How to sort out medical advice from medical nonsense”. Download. Also http://dx.doi.org/10.13140/RG.2.1.4904.8804
Constantinou, A., Freestone M., Marsh, W., Fenton, N. E. , Coid, J. (2015) "Risk assessment and risk management of violent reoffending among prisoners", Expert Systems With Applications 42 (21), 7511-7529. Pre-publication draft here. Published version: http://dx.doi.org/10.1016/j.eswa.2015.05.025
Fenton, N. E.(2014) "A Bayesian Network for a simple example of Drug Economics Decision Making", working paper DOI: http://10.13140/RG.2.1.1130.1281
Yet, B., Perkins Z., Fenton, N.E., Tai, N., Marsh, W., (2014) "Not Just Data: A Method for Improving Prediction with Knowledge", Journal of Biomedical Informatics, Vol 48, 28-37 http://dx.doi.org/10.1016/j.jbi.2013.10.012 (see here for details of model)
Yet, B., Perkins Z.,Marsh, W., Fenton, N.E., "Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support", ProBioMed 11, Bled, Slovenia, July 2011
Fenton, N. and Neil, M. (2010). "Comparing risks of alternative medical diagnosis using Bayesian arguments." Journal of Biomedical Informatics, 43: 485-495, http://dx.doi.org/10.1016/j.jbi.2010.02.004
Preprint here.