Dr William MarshSenior LecturerEmail: d.w.r.marsh@qmul.ac.ukTelephone: +44 20 7882 5254Room Number: Peter Landin, CS 420aWebsite: http://www.eecs.qmul.ac.uk/~williamOffice Hours: Wednesday 10:00-12:00TeachingResearchPublicationsTeachingEmbedded Systems (Postgraduate/Undergraduate)This module provides a practice-oriented introduction to embedded real-time systems. The main topics are (1) Modelling and simulation in UML and state-of-the-art tools; (2) Basic concepts of micro-controllers; (3) Real-time systems with interrupts and schedulers; (4) Real-time operating systems: processes and communication; (5) Energy aware design and construction; (6) Debugging and testing as part of software development processes.Statistics for Artificial Intelligence and Data Science (Postgraduate)This module has two components. The first introduces students to the use of probability and statistics in the context of data analysis. The module starts with basics of descriptive statistics and probability distributions. Then we go on with applied statistics techniques, such as visualisation, fitting probability distributions, time-series analysis, and hypothesis testing, which are all fundamental to the exploration, insight extraction, and modelling activities that are fundamental in handling data, of any size. The second covers some basic matrix algebra, including matrix multiplication and diagonalisation.ResearchResearch Interests:Medical Decision Support Models: Data, Knowledge and Evidence Can data be used for decision-making? In many applications there are not enough data, key values are not directly observed or the problem requires reasoning about change. In these cases, it is better to combine data and knowledge for building a decision model. Many medical decision problems fit this pattern. However, given the long history of clinical trials, clinicians are reluctant to assume an understanding of causes even when trials are completely impractical. Recent work on decision making in trauma surgery has shown the potential of causal models implemented using Bayesian networks. However, there are still many challenges before the use of these models can become routine. Safety, Reliability And Risk: Modelling Accidents & Incident Causes Analysing what can go wrong is fundamental for assessing risk in safety systems. Existing approaches have a number of deficiencies: (1) human behaviour and technical failures are poorly integrated (2) model created for system approval are often not used when a system is in operation (3) information on incidents and procedural compliance is not used to update risks. The aim of the research is to extend existing accident-based modelling techniques to resolve these problems. Recent work has proposed a new model structure using a Bayesian network for causal modelling from accident / incident data, with the aim of predicting the likely safety / reliability improvement that would be achieved by changes in the operation of a system at a specific location.Publications Hill A, Morrissey D, Marsh W (2024). What characteristics of clinical decision support system implementations lead to adoption for regular use? A scoping review. nameOfConference DOI: 10.1136/bmjhci-2024-101046 QMRO: qmroHref Lowe C, Sephton R, Marsh W et al. (publicationYear). Evaluation of a Musculoskeletal Digital Assessment Routing Tool (DART): Crossover Noninferiority Randomized Pilot Trial. nameOfConference DOI: 10.2196/56715 QMRO: qmroHref Wohlgemut JM, Pisirir E, Stoner RS et al. (publicationYear). Bayesian networks may allow better performance and usability than logistic regression. nameOfConference DOI: 10.1186/s13054-024-05015-w QMRO: qmroHref Lowe C, Browne M, Marsh W et al. (2024). CSP2023: 28 Digital Health Technology - Narrowing or Widening the Digital Divide? Learning From Validation of a Musculoskeletal Digital Assessment Tool (DART). nameOfConference DOI: 10.1016/j.physio.2024.04.105 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/99291 Wohlgemut JM, Pisirir E, Stoner RS et al. (2024). Identification of major hemorrhage in trauma patients in the prehospital setting: diagnostic accuracy and impact on outcome. nameOfConference DOI: 10.1136/tsaco-2023-001214 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/98191 Kyrimi E, Stoner RS, Perkins ZB et al. (2024). Updating and recalibrating causal probabilistic models on a new target population. nameOfConference DOI: 10.1016/j.jbi.2023.104572 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/93153 MacBrayne A, Curzon P, Soyel H et al. (publicationYear). Attitudes to technology supported rheumatoid arthritis care: investigating patient and clinician perceived opportunities and barriers. nameOfConference DOI: 10.1093/rap/rkad089 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/92300 Hill A, Joyner CH, Keith-Jopp C et al. (publicationYear). Assessing Serious Spinal Pathology Using Bayesian Network Decision Support: Development and Validation Study. nameOfConference DOI: 10.2196/44187 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/89908 Tandle S, Wohlgemut JM, Marsden MER et al. (publicationYear). Enhancing the clinical relevance of haemorrhage prediction models in trauma. nameOfConference DOI: 10.1186/s40779-023-00476-6 QMRO: qmroHref Wohlgemut J, McMullen H, Marsden M et al. (2023). FTP1.7 Critical decision analysis of pre-hospital major haemorrhage protocol activation. nameOfConference DOI: 10.1093/bjs/znad241.335 QMRO: qmroHref Wohlgemut JM, Pisirir E, Kyrimi E et al. (2023). Methods used to evaluate usability of mobile clinical decision support systems for healthcare emergencies: a systematic review and qualitative synthesis. nameOfConference DOI: 10.1093/jamiaopen/ooad051 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/90672 Pisirir E, Wohlgemut JM, Kyrimi E et al. (2023). A Process for Evaluating Explanations for Transparent and Trustworthy AI Prediction Models. 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI) DOI: 10.1109/ichi57859.2023.00058 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/94698 Curzon P, Macbrayne A, Humby F et al. (2023). Attitudes to Technology supported Rheumatoid Arthritis care Questionnaire study: Barriers to People with RA & their clinicians using Technology in the care pathway. British Society for Rheumatology Annual COnference 2023 DOI: 10.1093/rheumatology/kead104.154 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/84912 Curzon P, Macbrayne A, Soyel H et al. (2023). Attitudes to Technology supported Rheumatoid Arthritis care, questionnaire study: Opportunities for technology to improve RA care. British Society for Rheumatology Annual Conference, 2023 DOI: 10.1093/rheumatology/kead104.322 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/84913 Curzon P, Macbrayne A, Soyel H et al. (2023). Attitudes to Technology supported Rheumatoid Arthritis care: Opportunities & Barriers for technology in RA -Key themes from Qualitative arm of Mixed-Methods Study. British SOciety for Rheumatology Annual COnference, 2023 DOI: 10.1093/rheumatology/kead104.174 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/84914 Wohlgemut JM, Marsden MER, Stoner RS et al. (publicationYear). Diagnostic accuracy of clinical examination to identify life- and limb-threatening injuries in trauma patients. nameOfConference DOI: 10.1186/s13049-023-01083-z QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/85691 Yücetürk H, Gülle H, Şakar CT et al. (2022). Reducing the question burden of patient reported outcome measures using Bayesian networks. nameOfConference DOI: 10.1016/j.jbi.2022.104230 QMRO: qmroHref Marsden M, Perkins Z, Marsh W et al. (2022). 3* Evaluation of an Artificial Intelligence (AI) system to augment clinical risk prediction of Trauma Induced Coagulopathy in the pre-hospital setting: a prospective observational study. nameOfConference DOI: 10.1136/bmjmilitary-2022-rsmabstracts.3 QMRO: qmroHref Lowe C, Browne M, Marsh W et al. (publicationYear). Usability Testing of a Digital Assessment Routing Tool for Musculoskeletal Disorders: Iterative, Convergent Mixed Methods Study. nameOfConference DOI: 10.2196/38352 QMRO: qmroHref Ronaldson A, Freestone M, Zhang H et al. (publicationYear). Authors' Response to Peer Reviews of “Using Structural Equation Modelling in Routine Clinical Data on Diabetes and Depression: Observational Cohort Study”. nameOfConference DOI: 10.2196/38010 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/79008 Wohlgemut JM, Kyrimi E, Stoner RS et al. (2022). The outcome of a prediction algorithm should be a true patient state rather than an available surrogate. nameOfConference DOI: 10.1016/j.jvs.2021.10.059 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/85873 Marsden M, Perkins Z, Marsh W et al. (2022). 92 Evaluation of an Artificial Intelligence (AI) System to Augment Clinical Risk Prediction of Trauma Induced Coagulopathy: A Prospective Observational Study. nameOfConference DOI: 10.1093/bjs/znac041.005 QMRO: qmroHref Gulle H, Yuceturk H, Sakar C et al. (2022). Can Bayesian statistical approaches reduce the questionnaire burden for respondents when PROMs and PREMs are administered electronically?. nameOfConference DOI: 10.1016/j.physio.2021.12.019 QMRO: qmroHref Hill A, Joyner C, Yet B et al. (2022). 29 Managing serious pathology in low back pain: development and validation of a bayesian network decision support tool. Abstracts DOI: 10.1136/bmjsem-2022-sportskongres.5 QMRO: qmroHref MacBrayne A, Marsh W, Humby F (2022). Review: Remote disease monitoring in rheumatoid arthritis. nameOfConference DOI: 10.4103/injr.injr_142_21 QMRO: qmroHref Lowe C, Sing HH, Marsh W et al. (publicationYear). Validation of a Musculoskeletal Digital Assessment Routing Tool: Protocol for a Pilot Randomized Crossover Noninferiority Trial. nameOfConference DOI: 10.2196/31541 QMRO: qmroHref Hill A, Keith-Jopp C, Joyner C et al. (2021). Developing BENDi: A BayEsian Network DecIsion support tool for managing low back pain. nameOfConference DOI: 10.1016/j.physio.2021.10.052 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/75772 Daley BJ, Ni’Man M, Neves MR et al. (2022). mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review. nameOfConference DOI: 10.1111/dme.14735 QMRO: qmroHref Neves MR, Daley BJ, Hitman GA et al. (2021). Causal Dynamic Bayesian Networks for the Management of Glucose Control in Gestational Diabetes. 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) DOI: 10.1109/ichi52183.2021.00018 QMRO: qmroHref Lowe C, Sing HH, Browne M et al. (publicationYear). Usability Testing of a Digital Assessment Routing Tool: Protocol for an Iterative Convergent Mixed Methods Study. nameOfConference DOI: 10.2196/27205 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/72079 Kyrimi E, Dube K, Fenton N et al. (2021). Bayesian networks in healthcare: What is preventing their adoption?. nameOfConference DOI: 10.1016/j.artmed.2021.102079 QMRO: qmroHref Hill A, Joyner CH, Keith-Jopp C et al. (publicationYear). A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study. nameOfConference DOI: 10.2196/21804 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/75771 Fahmi A, MacBrayne A, Kyrimi E et al. (2020). Causal Bayesian Networks for Medical Diagnosis: A Case Study in Rheumatoid Arthritis. 2020 IEEE International Conference on Healthcare Informatics (ICHI) DOI: 10.1109/ichi48887.2020.9374327 QMRO: qmroHref Fahmi A, Soyel H, Marsh DWR et al. (2020). From Personalised Predictions to Targeted Advice: Improving Self-Management in Rheumatoid Arthritis. Integrated Citizen Centered Digital Health and Social Care DOI: 10.3233/SHTI200695 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/69045 Fahmi A, Soyel H, Marsh W et al. (2020). From personalised predictions to targeted advice: Improving self-management in rheumatoid arthritis. nameOfConference DOI: 10.3233/SHTI200695 QMRO: https://qmro.qmul.ac.uk/xmlui/handle/123456789/69046 Zhang H, Marsh DWR (2021). Managing Infrastructure Asset: Bayesian Networks for Inspection and Maintenance Decisions Reasoning and Planning. nameOfConference DOI: 10.1016/j.ress.2020.107328 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/69220 Ronaldson A, Freestone MC, Zhang H et al. (publicationYear). Using structural equation modelling in routine clinical data: Depression, diabetes, and use of Accident & Emergency (Preprint). nameOfConference DOI: 10.2196/22912 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/69430 Zhang H, Marsh DWR (2020). Multi-state deterioration prediction for infrastructure asset: Learning from uncertain data, knowledge and similar groups. nameOfConference DOI: 10.1016/j.ins.2019.11.017 QMRO: qmroHref Perkins ZB, Yet B, Sharrock A et al. (2020). Predicting the Outcome of Limb Revascularization in Patients With Lower-extremity Arterial Trauma: Development and External Validation of a Supervised Machine-learning Algorithm to Support Surgical Decisions.. nameOfConference DOI: 10.1097/sla.0000000000004132 QMRO: qmroHref Kyrimi E, Raniere Neves M, Mclachlan S et al. (2020). Medical idioms for clinical Bayesian network development. nameOfConference DOI: 10.1016/j.jbi.2020.103495 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/65612 Wilk M, Marsh DW, de Freitas S et al. (2020). Predicting length of stay in hospital using electronic records available at the time of admission. nameOfConference DOI: 10.3233/SHTI200186 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/67918 Waite J, Curzon P, Marsh W et al. (2020). Difficulties with design: The challenges of teaching design in K-5 programming. nameOfConference DOI: 10.1016/j.compedu.2020.103838 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/62819 Kyrimi E, Mossadegh S, Tai N et al. (2020). An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making. nameOfConference DOI: 10.1016/j.artmed.2020.101812 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/69918 Perkins ZB, Yet B, Marsden M et al. (2021). Early Identification of Trauma-induced Coagulopathy. nameOfConference DOI: 10.1097/sla.0000000000003771 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/62619 Neves MR, Marsh DWR (2019). Modelling the Impact of AI for Clinical Decision Support. nameOfConference DOI: 10.1007/978-3-030-21642-9_37 QMRO: qmroHref Zhang H, Marsh DWR (2018). Generic Bayesian network models for making maintenance decisions from available data and expert knowledge. nameOfConference DOI: 10.1177/1748006x17742765 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/28444 Waite JL, CURZON P, MARSH DW et al. (2018). Comparing K-5 teachers’ reported use of design in teaching programming and planning in teaching writing. WiPSCE 2018 (13th Workshop in Primary and Secondary Computing Education) DOI: 10.1145/3265757.3265761 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/54383 Marsden MER, Mossadegh S, Marsh W et al. (2020). Development of a major incident triage tool: the importance of evidence from implementation studies. nameOfConference DOI: 10.1136/jramc-2018-001057 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/47923 Perkins ZB, Yet B, Glasgow S et al. (2018). Long-term, patient-centered outcomes of lower-extremity vascular trauma. nameOfConference DOI: 10.1097/ta.0000000000001956 QMRO: qmroHref ZHANG H, MARSH DWR (2018). Towards A Model-Based Asset Deterioration Framework Represented by Probabilistic Relational Models. European Safety and Reliability Conference ESREL 2018 DOI: 10.1201/9781351174664-83 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/45945 MCLACHLAN S, Potts HWW, Dube K et al. (2018). The Heimdall framework for supporting characterisation of learning health systems. nameOfConference DOI: 10.14236/jhi.v25i2.996 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/40805 Waite JL, CURZON P, MARSH D et al. (2018). Abstraction in action: K-5 teachers' uses of levels of abstraction, particularly the design level, in teaching programming. nameOfConference DOI: 10.21585/ijcses.v2i1.23 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/32505 McLachlan S, Dube K, Buchanan D et al. (2018). Learning Health Systems: The research community awareness challenge.. nameOfConference DOI: 10.14236/jhi.v25i1.981 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/59240 Yet B, Marsh W, Morrissey D (2018). Towards an Evidence-Based Decision Support Tool for Management of Musculoskeletal Conditions.. nameOfConference DOI: 10.3233/978-1-61499-921-8-175 QMRO: qmroHref Waite JL, curzon P, marsh D et al. (2017). K-5 Teachers' Uses of Levels of Abstraction Focusing on Design. WiPSCE 2017 DOI: doi QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/28463 Zhang H, Marsh DWR (2017). Bayesian network models for making maintenance decisions from data and expert judgment. nameOfConference DOI: doi QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/13065 Perkins ZB, Yet B, Glasgow S et al. (2017). Predicting limb viability following lower extremity vascular trauma. nameOfConference DOI: doi QMRO: qmroHref Coid JW, Ullrich S, Kallis C et al. (2016). Improving risk management for violence in mental health services: a multimethods approach. nameOfConference DOI: 10.3310/pgfar04160 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/18327 Waite JL, Curzon P, marsh D et al. (2016). Abstraction and Common Classroom Activities. WiPSCE 2016 11th Workshop in Primary and Secondary Computing Education DOI: 10.1145/2978249.2978272 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/15115 Fenton N, Neil M, Lagnado D et al. (2016). How to model mutually exclusive events based on independent causal pathways in Bayesian network models. nameOfConference DOI: 10.1016/j.knosys.2016.09.012 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/15923 MARSH DWR, Kyrimi E (publicationYear). A Progressive Explanation of Inference in ‘Hybrid’ Bayesian Networks for Supporting Clinical Decision Making. Conference on Probabilistic Graphical Models DOI: doi QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/15046 Mossadegh S, Yet B, Perkins Z et al. (2016). Predictive Accuracy of a Civilian Bayesian Network Trauma Tool in a Military Cohort and Applicability to Trauma Performance Improvement. nameOfConference DOI: doi QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/31408 Marsh W, Nur K, Yet B et al. (2016). Using operational data for decision making: a feasibility study in rail maintenance. nameOfConference DOI: 10.1080/09617353.2016.1148923 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/12483 Mossadegh S, Kyrimi E, Marsh W et al. (2016). Implementation science: a Bayesian prediction tool for acute traumatic coagulopathy. Society of Academic and Research Surgery Annual Meeting DOI: 10.1002/bjs.10158 QMRO: qmroHref Yet B, Perkins ZB, Tai NRM et al. (2016). Clinical evidence framework for Bayesian networks. nameOfConference DOI: 10.1007/s10115-016-0932-1 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/22339 Constantinou AC, Fenton N, Marsh W et al. (2016). From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. nameOfConference DOI: 10.1016/j.artmed.2016.01.002 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/11587 Ahmed N, Shamsujjoha, Ali NY et al. (2015). An Efficient REDCap Based Data Collection Platform for the Primary Immune Thrombocytopenia and its Analysis Over the Conventional Approaches. 2015 18th International Conference on Computer and Information Technology (ICCIT) DOI: 10.1109/iccitechn.2015.7488095 QMRO: qmroHref CONSTANTINOU AC, Freestone M, Marsh W et al. (2015). Causal inference for violence risk management and decision support in forensic psychiatry. nameOfConference DOI: 10.1016/j.dss.2015.09.006 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/10774 Constantinou AC, Yet B, Fenton N et al. (2015). Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences. nameOfConference DOI: 10.1016/j.artmed.2015.09.002 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/10760 Constantinou AC, Freestone M, Marsh W et al. (2015). Risk assessment and risk management of violent re-offending among prisoners. nameOfConference DOI: 10.1016/j.eswa.2015.05.025 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/15804 Perkins ZB, Yet B, Glasgow S et al. (2015). Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma. nameOfConference DOI: 10.1002/bjs.9689 QMRO: qmroHref Perkins ZB, Yet B, Glasgow S et al. (2015). Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma. nameOfConference DOI: 10.1002/bjs.9689 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/23403 Yet B, Perkins ZB, Rasmussen TE et al. (2014). Combining data and meta-analysis to build Bayesian networks for clinical decision support.. nameOfConference DOI: 10.1016/j.jbi.2014.07.018 QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/23055 Yet B, Perkins Z, Fenton N et al. (2014). Not just data: a method for improving prediction with knowledge.. nameOfConference DOI: 10.1016/j.jbi.2013.10.012 QMRO: qmroHref Yet B, Marsh DWR (2014). Compatible and incompatible abstractions in Bayesian networks. nameOfConference DOI: 10.1016/j.knosys.2014.02.020 QMRO: qmroHref Yet B, Perkins Z, Tai N et al. (2014). Explicit evidence for prognostic Bayesian network models.. nameOfConference DOI: 10.3233/978-1-61499-432-9-53 QMRO: qmroHref Yet B, Perkins Z, Fenton N et al. (2014). Not just data: A method for improving prediction with knowledge. nameOfConference DOI: 10.1016/j.jbi.2013.10.012 QMRO: qmroHref Perkins ZB, Yet B, Glasgow S et al. (2014). Prognostic Factors for Amputation Following Surgical Repair of Lower Extremity Vascular Trauma: A Systematic Review and Meta-Analysis of Observational Studies. nameOfConference DOI: 10.1016/j.jvs.2014.03.167 QMRO: qmroHref Winther R, Marsh W (publicationYear). Hazards, accidents and events-a land of confusing terms. nameOfConference DOI: 10.1201/b15938-380 QMRO: qmroHref Bearfield G, Holloway A, Marsh W (2013). Change and safety: decision-making from data. nameOfConference DOI: 10.1177/0954409713498381 QMRO: qmroHref MARSH DWR, Yet B, Bastani K et al. (2013). Decision Support System for Warfarin Therapy Management Using Bayesian Networks. nameOfConference DOI: 10.1016/j.dss.2012.10.007 QMRO: qmroHref Perkins Z, Yet B, Glasgow S et al. (2013). EARLY PREDICTION OF TRAUMATIC COAGULOPATHY USING ADMISSION CLINICAL VARIABLES. nameOfConference DOI: doi QMRO: qmroHref Sivell S, Marsh W, Edwards A et al. (2012). Theory-based design and field-testing of an intervention to support women choosing surgery for breast cancer: BresDex. nameOfConference DOI: 10.1016/j.pec.2011.04.014 QMRO: qmroHref Bearfield G, Marsh W (2010). Causal Modelling of Lower Consequence Rail Safety Incidents. European Safety and Reliability Conference 2010 (ESREL 2010) DOI: doi QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/1340 Marsh DWR, Bearfield GJ (2009). Why Risk Models should be Parameterised. MASR-2009 Modeling and Analysis of Safety and Risk in Complex Systems DOI: doi QMRO: https://uat2-qmro.qmul.ac.uk/xmlui/handle/123456789/1145 Fenton N, Neil M, Marsh W et al. (2008). On the effectiveness of early life cycle defect prediction with Bayesian Nets. nameOfConference DOI: 10.1007/s10664-008-9072-x QMRO: qmroHref Marsh DWR, Bearfield G (2008). Generalizing event trees using Bayesian networks. nameOfConference DOI: 10.1243/1748006XJRR131 QMRO: qmroHref Marsh DWR, Bearfield G (2008). Generalizing event trees using Bayesian networks. nameOfConference DOI: doi QMRO: qmroHref Dray P, Bearfield GJ, Marsh DWR (2007). Constructing Scalable and Parameterised System Wide Risk Models. nameOfConference DOI: doi QMRO: qmroHref Marsh DWR, Bearfield GJ (2007). Merging event trees using Bayesian networks. nameOfConference DOI: doi QMRO: qmroHref Fenton N, Neil M, Marsh W et al. (2007). Predicting software defects in varying development lifecycles using Bayesian nets. nameOfConference DOI: 10.1016/j.infsof.2006.09.001 QMRO: qmroHref Fenton NE, Neil M, Marsh W et al. (2007). Project Data Incorporating Qualitative Factors for Improved Software Defect Prediction. ICSE PROMISE (Predictive Models in Software Engineering) 07 DOI: 10.1109/PROMISE.2007.11 QMRO: qmroHref Marsh W, Bearfield G (2007). Representing parameterised fault trees using Bayesian networks. nameOfConference DOI: 10.1007/978-3-540-75101-4_13 QMRO: qmroHref Neil M, Fenton N, MARSH DWR (2006). A Software Metrics Challenge: Data for Project Prediction. 29th International Conference on Software Engineering (ICSE 2007), Minneapolis, USA DOI: doi QMRO: qmroHref Neil M, FENTON NE, Marsh W et al. (2006). Predicting Software Defects in Varying Development Lifecycles using Bayesian Nets. ICSE (International Conference on Software Engineering) 2006, May 20-28, 2006, Shanghai, China DOI: doi QMRO: qmroHref Bearfield G, Marsh W (2005). Generalising event trees using Bayesian networks with a case study of train derailment. nameOfConference DOI: 10.1007/11563228_5 QMRO: qmroHref Fenton N, Marsh W, Neil M et al. (2004). Making resource decisions for software projects. nameOfConference DOI: 10.1109/icse.2004.1317462 QMRO: qmroHref Marsh W, Bearfield G (2004). Using Bayesian networks to model accident causation in the UK railway industry. nameOfConference DOI: 10.1007/978-0-85729-410-4_575 QMRO: qmroHref Marsh W (1997). Harmonisation of defence standards for safety-critical software. nameOfConference DOI: 10.1016/s0141-9331(97)00018-5 QMRO: qmroHref WICHMANN B, CANNING A, CLUTTERBUCK D et al. (1995). Industrial Perspective on Static Analysis. nameOfConference DOI: doi QMRO: qmroHref CARRE B, GARNSWORTHY J, MARSH W (1992). SPARK - A SAFETY - RELATED ADA SUBSET. nameOfConference DOI: doi QMRO: qmroHref