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Institute of Applied Data Science

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Dr William Marsh

William Marsh

Senior Lecturer in Computer Science & Turing Fellow

Department School of Electronic Engineering and Computer Science
 Room CS/422
Telephone +44 (0)20 7882 5254

www.eecs.qmul.ac.uk/~william/

Research

techniques for risk models, Bayesian networks, decision support systems, critical systems

Interests

Dr William Marsh is Principal Investigator on a new project funded by the Alan Turing Institute: 'Knowledge Discovery from Health Use Data' (KNIFE). 

The increasing use and capability of Electronic Health Record (EHR) systems has made available large collections of data about patients’ use of the different health services, treatments and prescriptions. This data has many potential uses (discovering causes, optimising health delivery, choosing treatment and more). However, there are challenges to overcome before these benefits can be achieved.

• Challenge of Understanding the Data and It Potential Use. The data arise from the operation of the health services, so the understanding of the data contents is embedded in the health community and not accessible to the wider AI and ML communities.
• Challenge of Knowledge Elicitation and Modelling. Achieving the full potential from the data
requires knowledge of health care processes so this may need to be modelled for data analysis.
• Statistical Modelling Challenge. Existing data analysis approaches often extract a ‘flat’ dataset from the underlying ‘relational’ structure of the date. New techniques avoiding this (e.g. Statistical Relational Learning) might allow new types of queries but their practical applicability is unknown.
• Challenge of Efficient and Acceptable Data Handling. Existing studies using data from EHR systems, require extensive ‘data wrangling’ to extract and cleanse a usable dataset. This work is largely manual and very time consuming: can this be improved?
The overall objective of the project is to lay the foundations for a transformative approach to patient-linked health data, making it accessible for both medical and data science researchers to fully exploit.

William's other research aims are to develop better ways to build useful risk and decision making techniques, using a combination of data and knowledge (or expertise). He mainly works with Bayesian networks and prefers to work with the 'end users' who are making decisions. He is currently collaborating with several groups of clinicians to build decision support systems for medical decision problems and also collaborates with several industry groups on techniques for risk models and other aspects of Safety Engineering, primarily in the railway industry.

Publications are here 

Publications

Publications of specific relevance to Applied Data Science

2020

Zhang H and Marsh DWR (2020). Multi-state deterioration prediction for infrastructure asset: Learning from uncertain data, knowledge and similar groups. Information Sciences  vol. 529, 197-213. 10.1016/j.ins.2019.11.017
Kyrimi E, Raniere Neves M, Mclachlan S, Neil M, Marsh W and Fenton N (2020). Medical idioms for clinical Bayesian network development. Elsevier  Journal of Biomedical Informatics  10.1016/j.jbi.2020.103495
Wilk M, Marsh DWR, De Freitas S and Prowle J (2020). Predicting Length of Stay in Hospital Using Electronic Records Available at the Time of Admission. Studies in Health Technology and Informatics  vol. 270, 377-381. 10.3233/SHTI200186
Marsden MER, Marsden MER, Mossadegh S, Marsh W and Tai N (2020). Development of a major incident triage tool: The importance of evidence from implementation studies. Bmj Military Health  vol. 166, (3) 10.1136/jramc-2018-001057
Kyrimi E, Mossadegh S, Tai N and Marsh W (2020). An incremental explanation of inference in Bayesian networks for increasing model trustworthiness and supporting clinical decision making. Artificial Intelligence in Medicine  vol. 103, 10.1016/j.artmed.2020.101812
Waite J, Curzon P, Marsh W and Sentance S (2020). Difficulties with design: The challenges of teaching design in K-5 programming. Elsevier Bv  Computers & Education  103838-103838. 10.1016/j.compedu.2020.103838
Perkins ZB, Yet B, Marsden M, Glasgow S, Marsh W, Davenport R, Brohi K and Tai NRM (2020). Early Identification of Trauma-induced Coagulopathy: Development and Validation of a Multivariable Risk Prediction Model. Ann Surg  10.1097/SLA.0000000000003771

2018

Zhang H and Marsh DWR (2018). Generic Bayesian network models for making maintenance decisions from available data and expert knowledge. Proceedings of The Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability  vol. 232, (5) 505-523. 10.1177/1748006X17742765
Perkins ZB, Yet B, Glasgow S, Marsh DWR, Tai NRM and Rasmussen TE (2018). Long-term, patient-centered outcomes of lower-extremity vascular trauma. J Trauma Acute Care Surg  vol. 85, (1S Suppl 2) S104-S111. 10.1097/TA.0000000000001956
MCLACHLAN S, Potts HWW, Dube K, Buchanan D, Lean S, Gallagher T, Johnson O, DALEY B, Marsh W and FENTON N (2018). The Heimdall framework for supporting characterisation of learning health systems. Bcs Journal of Innovation in Health Informatics  vol. 25, (2) 10.14236/jhi.v25i2.996
Waite JL, CURZON P, MARSH D, Sentance S and Hawden-Bennett A (2018). Abstraction in action: K-5 teachers' uses of levels of abstraction, particularly the design level, in teaching programming., Editors: Kalelioglu F and Allsop Y. International Journal of Computer Science Education in Schools  vol. 2, (1) 14-40. 10.21585/ijcses.v2i1.23
McLachlan S, Dube K, Buchanan D, Lean S, Johnson O, Potts H, Gallagher T, Marsh W and Fenton N (2018). Learning health systems: The research community awareness challenge. Journal of Innovation in Health Informatics  vol. 25, (1) 38-40. 10.14236/jhi.v25i1.981
Yet B, Marsh W and Morrissey D (2018). Towards an Evidence-Based Decision Support Tool for Management of Musculoskeletal Conditions. Studies in Health Technology and Informatics  vol. 255, 175-179. 10.3233/978-1-61499-921-8-175

2017

Waite JL, curzon P, marsh D and Sentance S (2017). K-5 Teachers' Uses of Levels of Abstraction Focusing on Design.

2016

Fenton N, Neil M, Lagnado D, William M, Yet B and CONSTANTINOU AC (2016). How to model mutually exclusive events based on independent causal pathways in Bayesian network models. Knowledge-Based Systems  10.1016/j.knosys.2016.09.012
Mossadegh S, Yet B, Perkins Z, Marsh W and Tai N (2016). Predictive Accuracy of a Civilian Bayesian Network Trauma Tool in a Military Cohort and Applicability to Trauma Performance Improvement. British Journal of Surgery  vol. 103, 96-96.
Yet B, Perkins ZB, Tai NRM and Marsh DWR (2016). Clinical evidence framework for Bayesian networks. Knowledge and Information Systems  vol. 50, (1) 117-143. 10.1007/s10115-016-0932-1
Constantinou AC, Fenton N, Marsh W and Radlinski L (2016). From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artificial Intelligence in Medicine  vol. 67, 75-93. 10.1016/j.artmed.2016.01.002
Marsh W, Nur K, Yet B and Majumdar A (2016). Using operational data for decision making: a feasibility study in rail maintenance. Informa Uk Limited  Safety and Reliability  vol. 36, (1) 35-47. 10.1080/09617353.2016.1148923

2015

CONSTANTINOU AC, Freestone M, Marsh W and Coid J (2015). Causal inference for violence risk management and decision support in forensic psychiatry. Decision Support Systems  vol. 80, 42-55. 10.1016/j.dss.2015.09.006
Constantinou AC, Yet B, Fenton N, Neil M and Marsh W (2015). Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences. Elsevier  Artificial Intelligence in Medicine  vol. 66, 41-52. 10.1016/j.artmed.2015.09.002
Constantinou AC, Freestone M, Marsh W, Fenton NE and Coid J (2015). Risk assessment and risk management of violent re-offending among prisoners. Expert Systems With Applications  vol. 42, (21) 10.1016/j.eswa.2015.05.025
Perkins ZB, Yet B, Glasgow S, Cole E, Marsh W, Brohi K, Rasmussen TE and Tai NRM (2015). Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma. British Journal of Surgery  vol. 102, (5) 436-450. 10.1002/bjs.9689
Perkins ZB, Yet B, Glasgow S, Cole E, Marsh W, Brohi K, Rasmussen TE and Tai NRM (2015). Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma. John Wiley and Sons Ltd  British Journal of Surgery  10.1002/bjs.9689

2014

Yet B, Perkins ZB, Rasmussen TE, Tai NRM and Marsh DWR (2014). Combining data and meta-analysis to build Bayesian networks for clinical decision support. J Biomed Inform  vol. 52, 373-385. 10.1016/j.jbi.2014.07.018
Yet B, Perkins Z, Fenton N, Tai N and Marsh W (2014). Not just data: a method for improving prediction with knowledge. J Biomed Inform  vol. 48, 28-37. 10.1016/j.jbi.2013.10.012
Yet B and Marsh DWR (2014). Compatible and incompatible abstractions in Bayesian networks. Knowledge-Based Systems  10.1016/j.knosys.2014.02.020
Yet B, Perkins Z, Fenton N, Tai N and Marsh W (2014). Not just data: A method for improving prediction with knowledge. Journal of Biomedical Informatics  vol. 48, 28-37. 10.1016/j.jbi.2013.10.012
Yet B, Perkins Z, Tai N and Marsh W (2014). Explicit evidence for prognostic Bayesian network models. Stud Health Technol Inform  vol. 205, 53-57. 10.3233/978-1-61499-432-9-53

2013

Bearfield G, Holloway A and Marsh W (2013). Change and safety: decision-making from data. Proceedings of The Institution of Mechanical Engineers Part F-Journal of Rail and Rapid Transit  vol. 227, (6) 704-714. 10.1177/0954409713498381
MARSH DWR, Yet B, Bastani K, Raharjo H, Lifvergren S and Bergman B (2013). Decision Support System for Warfarin Therapy Management Using Bayesian Networks. Elsevier  Decision Support Systems  vol. 55, (2) 488-498. 10.1016/j.dss.2012.10.007

2008

Fenton N, Neil M, Marsh W, Hearty P, Radlinski L and Krause P (2008). On the effectiveness of early life cycle defect prediction with Bayesian Nets. Empirical Software Engineering  vol. 13, (5) 499-537. 10.1007/s10664-008-9072-x
Marsh DWR and Bearfield G (2008). Generalizing event trees using Bayesian networks. Proceedings of The Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability  vol. 222, (2) 105-114. 10.1243/1748006XJRR131
Marsh DWR and Bearfield G (2008). Generalizing event trees using Bayesian networks. Proc. Imeche, Parto: J. Risk and Reliability  vol. 222, 105-114-105-114.

2007

Fenton N, Neil M, Marsh W, Hearty P, Marquez D, Krause P and Mishra R (2007). Predicting software defects in varying development lifecycles using Bayesian nets. Inform Software Tech  vol. 49, (1) 32-43. 10.1016/j.infsof.2006.09.001

1995

WICHMANN B, CANNING A, CLUTTERBUCK D, WINSBORROW L, WARD N and MARSH D (1995). Industrial Perspective on Static Analysis. Iee-Inst Elec Eng  Software Engineering Journal  vol. 10, (2) 69-75.

Grants

Grants of specific relevance to Applied Data Science
PAMBAYESIAN: PAtient Managed decision-support using Bayesian networks
Fenton NE, Collier DJ, Neil M, Patel A, Hitman GA, Humby FC, Huda MSB, Brown VT, Curzon P, Alomainy A, Morrissey D and Marsh W
£1,538,497 Engineering and Physical Sciences Research Council (30-06-2017 - 31-12-2020)
Summary
Knowledge Transfer Account - Queen Mary, University of London
Gillin WP and Marsh W
£2,921,292 Engineering and Physical Sciences Research Council (30-09-2009 - 31-03-2013)
Summary


DIADEM: Data Information and Analysis for clinical DEcision Making
Fenton NE and Marsh W
£161,382 Engineering and Physical Sciences Research Council (31-03-2008 - 30-03-2009)
Summary