Publications – PAMBAYESIAN (Patient Managed Decision-Support using Bayesian Networks)

December 7, 2017 / byEugene / 0

2021

Kambarami, F., McLachlan, S., Bozic, B., Dube, K., & Chimhundu, H. (2021). Computational Modelling of Agglutinative Languages: The Challenge for Southern Bantu Languages. Arusha Working Papers in African Linguistics, 3(1), pp 52-81.

Wang, H., Neil. M., Fenton. NE., (2020) A Recursive Method for Approximate Inference in Discrete Dynamic Bayesian Networks using Interface Junction Trees
(under review, IEEE Transactions on Pattern Analysis and Machine Intelligence)

Hartmann M, Fenton NE and Dobson R. (2021) Current Review and Next Steps for Artificial Intelligence in Multiple Sclerosis Risk Research. Comput. Biol. Med. DOI: 10.1016/j.compbiomed.2021.104337

2020

McLachlan, S., Kyrimi, E., Dube, K., Hitman, G.A., Simmonds, J., & Fenton, N.E. (2020). Towards Standardisation of clinical care process specifications. SAGE Health Informatics Journal (HIJ) DOI: 10.1177/1460458220906069

Kyrimi, E., Mossadegh, S., Tai, N., & Marsh, W. (2020). An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making DOI: 10.1016/j.artmed.2020.101812

Lin, P., Neil, M., & Fenton, N.E. (2020). Improved High Dimensional Discrete Bayesian Network Inference using Triplet Region Construction. Journal of Artificial Intelligence Research, 69, 231–295. 10.1613/jair.1.12198

McLachlan, S., Kyrimi, E., Daley, B., Dube, K., Marsden, M., Finer, S., Hitman, G., & Fenton, N. (2020). Incorporating Clinical Decisions into Standardised Caremaps (No. 2745). Proceedings of the IEEE International Conference on Health Informatics (ICHI 2020). DOI: 10.1109/ICHI48887.2020.9374381

Osman, M., McLachlan, S., Fenton, N., Neil, M., Lofstedt, R., & Meder, B. (2020) Learning from behaviour changes that fail. Trends in Cognitive Science, 24(12). DOI: 10.1016/j.tics.2020.09.009

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 (AIIM), 107. DOI: 10.1016/j.artmed.2020.101912

Neil, M., Fenton, N., 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) DOI: 10.1080/13669877.2020.1778771

Osman, M., Fenton, N., McLachlan, S., Lucas, P., Dube, K., Hitman, G., Kyrimi, E., & Neil, M. (2020) The thorny problems of Covid-19 Contact Tracing Apps: The need for a holistic approach. Journal of Behavioural Economics for Policy, 4, COVID-19 Special Issue, pp 57-61

Daley, B., Kyrimi, E., Dube, K., Fenton, N., Hitman, G.A., & McLachlan, S. (2020). Data visualisation in midwifery: The challenge of seeing what datasets hide. Digital Personalised Health and Medicine, Medical Informatics Europe (MIE) 2020 Conference in Geneva, Switzerland. DOI: 10.3233/SHTI200381

Fahmi, A., MacBrayne, A., Kyrimi, E., McLachlan, S., Humby, F., & Marsh, W. (2020). Causal Bayesian networks for medical diagnosis: A case study in Rheumatoid Arthritis. Proceedings of the IEEE International Conference on Health Informatics (ICHI 2020). DOI: 10.1109/ICHI48887.2020.9374327

McLachlan, S., Paterson, H., Dube, K., Kyrimi, E., Dementiev, E., Neil, M., Daley, B., Hitman, G.A., & Fenton, N. (2020). Real-time Online Probabilistic Medical Computation using Bayesian Networks (No. 2744). Proceedings of the IEEE International Conference on Health Informatics (ICHI 2020). DOI: 10.1109/ICHI48887.2020.9374378

Fenton, N., Osman, M., Neil, 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, 23(7-8) DOI: 10.1080/13669877.2020.1756381

McLachlan, S., Kyrimi, E., Dube, K., & Fenton, N. (2020). Standardising Clinical Caremaps: Model, Method and Graphical Notation for Caremap Specification. Communications in Computer and Information Science, 1212, pp 429-452. Springer Nature, DE.

McLachlan, S. (2020). Health information standardisation as a basis for learning health systems. A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy. Queen Mary University of London. https://arxiv.org/abs/2004.04811

Fenton, N., Osman, M., Neil, M., & McLachlan, S. (2020). Coronavirus: Our study suggests more people have had it than previously estimated. The Conversation, (June 25th, 2020). https://theconversation.com/coronavirus-our-study-suggests-more-people-have-had-it-than-previously-estimated-140996

Kyrimi, E., Neves, M., McLachlan, S., Neil, M., Marsh, W., & Fenton, N. (2020). Medical Idioms for clinical Bayesian Network Development. Journal of Biomedical Informatics, 108. DOI: 10.1016/j.jbi.2020.103495

2019

McLachlan, S., Dube, K., Gallagher, T., Simmonds, J., & Fenton, N. (2019) Realistic Synthetic Data Generation: The ATEN Framework. In: Cliquet, Jr. A. et al (eds) Communications in Computer and Information Science, vol 1024. Springer Nature, Cham. DOI: 10.1007/978-3-030-29196-9_25

Daley, B., Hitman, G.A., Fenton, N., & McLachlan, S. (2018) Assessment of the quality and content of national and international guidelines on the identification and management of Diabetes in Pregnancy: An AGREE II Study. BMJ Open, e:027285 DOI: 10.1136/bmjopen-2018-027285

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), Seattle, USA.

McLachlan, S., Dube, K., Gallagher, T., Simmonds, J., & Fenton, N. (2019) Realistic Synthetic Data Generation: The ATEN Framework. In: Cliquet, Jr. A. et al (eds) Communications in Computer and Information Science, vol 1024. Springer Nature, Cham. DOI: 10.1007/978-3-030-29196-9_25

Raniere, M., & Marsh, W. (2019) Modelling the impact of AI Systems for Clinical Decision Support. in Riaño D., Wilk S., ten Teije A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science, vol 11526. Springer, Cham. DOI: 10.1007/978-3-030-21642-9_37

McLachlan, S., Dube, K., Kyrimi, E., & Fenton, N. (2019). LAGOS: Learning Health Systems and how they can integrate with Patient Care. BMJ Health and Care Informatics (BMJHCI) 26(1)

McLachlan, S., Johnson, O., Dube, K., Buchanan, D., Potts, H.W.W., Gallagher, T., & Fenton, N. (2019). A framework for analysing learning health systems: Are we removing the most impactful barriers? Journal of Learning Health Systems. DOI: 10.1002/lrh2.10189

McLachlan, S., Kyrimi, E., Dube, K., & Fenton, N. (2019). Clinical caremap development: How can caremaps standardise care when they are not standardised? Proceedings of the 12th International Joint Conference on Biomedical Systems and Technologies (BIOSTEC 2019), volume 5: HEALTHINF, pp 123-134. DOI: 10.5220/0007522601230134

Noguchi, T., Fenton, N. E., & Neil, M. (2019). Addressing the Practical Limitations of Noisy-OR using Conditional Inter-causal Anti-Correlation with Ranked Nodes. IEEE Transactions on Knowledge and Data Engineering, 31(4): 813-817. DOI: 10.1109/TKDE.2018.2873314

Fenton, N. E., Noguchi, T. & Neil, M (2019). An extension to the noisy-OR function to resolve the “explaining away” deficiency for practical Bayesian network problems. IEEE Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2019.2891680

2018

McLachlan, S., Dube, K., Buchanan, D., Lean, S., Johnson, O., Potts, H.W.W., Gallagher, T., Marsh, W., & Fenton, N. (2018) Learning Health Systems: The research community awareness challenge. Journal of Innovation in Health Informatics, 25(1) DOI: 10.14236/jhi.v25i1.981.

McLachlan, S., Dube, K., Gallagher, T., Daley, B., & Walonoski, J. (2018) The ATEN Framework for Creating the Realistic Synthetic Electronic Health Record Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, HEALTHINF 2018

McLachlan, S., Kyrimi, E., Dube, K., & Fenton, N. (2019). Clinical caremap development: How can caremaps standardise care when they are not standardised? Proceedings of the 12th International Joint Conference on Biomedical Systems and Technologies (BIOSTEC 2019), volume 5: HEALTHINF, pp 123-134. DOI: 10.5220/0007522601230134

Yet, B., Constantinou, A., Fenton, N., & Neil, M. (2018). Expected Value of Partial Perfect Information in Hybrid Models using Dynamic Discretization. IEEE Access. DOI: 10.1109/ACCESS.2018.2799527

McLachlan, S., Potts, H.W.W., Dube, K., Buchanan, D., Lean, S., Gallagher, T., Johnson, O., Daley, B., Marsh, W., & Fenton, N. (2018) The Heimdall Framework for supporting characterisation of Learning Health Systems. Journal of Innovation in Health Informatics, 25(2). DOI: http://dx.doi.org/10.14236/jhi.v25i2.996

Gallagher, T., Dube, K., & McLachlan, S. (2018). Ethical Issues in Secondary Use of Personal Health Information. IEEE Future Directions.

Constantinou, A., Fenton, N.E, (2018) Things to know about Bayesian networks. Significance, 15(2)

Yet, B., Constantinou, A., Fenton, N. & Neil, M. (2018) Expected Value of Partial Perfect Information in Hybrid Models using Dynamic Discretization. IEEE Access, Vol 6, pp 7802-7817

Fenton, N. E., & Neil, M. (2018). How Bayesian Networks are pioneering the ‘smart data’ revolution. Open Access Government, July 2018 pages 22-23

2017

McLachlan, S., Dube, K., Buchanan, D., Lean, S., Johnson, O., Potts, H., Gallagher, T., Marsh, W., & Fenton, N. (2017) Learning Health Systems: The research community awareness challenge BCS Journal of Innovation in Health Informatics, 25(1). DOI: http://dx.doi.org/10.14236/jhi.v25i1.981

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