Professor Shang-Ming Zhou
Profiles

Professor Shang-Ming Zhou

Professor of e-Health

School of Nursing and Midwifery (Faculty of Health)

Biography

Biography

Welcome Prospective PhD Students
Shangming is interested in supervising strong potential UK and international PhD students from allied health professionals or computing science backgrounds. The areas of PhD studies include health data science, health and biomedical informatics, artificial intelligience (AI) for healthcare and medicine, quantitative data analysis and/or evaluation of e-health technologies, such as 
  • AI in health and care;
  • explainable machine learning (XAI) in healthcare; 
  •  ethical AI in healthcare;
  • electronic health records analytics;
  •  natural language processing /text mining in healthcare;
  • e-health technology transformation;
  • early detection and diagnosis;
  • multimorbidity and polypharmacy; 
  • disease phenotyping; 
  • patient safety;
  • etc. 
If you have an ambition in pursuing a PhD study in any related topic, you are most welcome to contact him via the email.

About
Currently, Shangming is the Director of NHS Kernow Datalab with the Centre for Health Technologyat the Faculty of Health: Medicine, Dentistry and Human Sciences. He is also an affiliated investigatorwith the Health Data Research UK(HDR UK). His research was funded by HDRUK, MRC, EPSRC, HCRW, Charities, and international collaborations.
Before joining the University of Plymouth, Shangming worked with the Scottish Digital Health and Care Institute and University of Strathclyde, Swansea University, De Montford University, University of Essex, and Chinese Academy of Sciences.
His primary scholarly interests are AI in health and biomedical informatics, health data science, biomedical statistics and information aggregation / integration via type-1 OWA operators and type-2 OWA operators. In implementation science, he is particularly interested in (big) data analytics and AI with electronic health data for personalised medicine, disease phenotyping, polypharmacy, multimorbidity, risk factors identification etc; clinical decision supports driven by type-1 OWA operators and type-2 OWA operators; machine learning and data mining applied to epidemiology and public health. In developmental domains, he is particularly interested in developing and using explainable/transparent machine learning (i.e. XAI), type-1/ type-2 OWA operators and other AI technologies for electronic health records and –omics data to extract personally useful information, such as rules and patterns, concerning lifestyles and health conditions to promote healthier lifestyles and prevent disease.
The medical conditions to which he is particularly interested in applying AI and biomedical statistics techniques include, but are not limited to, the long-term health conditions (such as cancer, dementia, epilepsy, asthma, diabetes, multiple sclerosis, mental health conditions etc.)
He was the recipient of “Best Paper Award” sponsored by Springer Nature at the International Conference on Frontiers of Intelligent Computing: Theory and Applications; “Best Poster Prize” at the Royal College of Physicians (RCP) Annual Conference; IFIP-WG8.9 “Outstanding Academic Service Award"; and “Outstanding Reviewer Award" from Journal of Biomedical Informatics; Journal of Science and Medicine in Sport; Fuzzy Sets and Systems; IEEE Transactions on Cybernetics; Applied Soft Computing, Knowledge Based Systems, Expert Systems with Applications, respectively.

PhD Students
  • Xu Wang (2022~2025) ‒ “Improving Medication Verification for Cancer Patients: An AI Led Population Health Study” (Director of Study) .
  • Xiatian Fan  (2023~2027) ‒ “Mining Routinely Collected Electronic Health Records to Identify Effective Dietetic Factors for Optimal Care in General Practice” (Director of Study) .
  • Tristan Coombe (2023~2029) ‒ “On the Use of Artificial Intelligence in Nursing Education” (Director of Study) .
  • Joan Jonathan Mnyambo (2023~2027) ‒“Prediction of Diagnostic Accuracy using Artificial Intelligence and Big Data Analytics from HeroRats for Tuberculosis Detection” (Second Supervisor) 

Qualifications

BSc, MSc, PhD, FHEA

Professional membership

  • Fellow of the Higher Education Academy
  • Member of IEEE (The Institute of Electrical and Electronics Engineers)
  • Member of IEEE Engineering in Medicine & Biology Society
  • Member of IEEE Computational Intelligence Society
  • Member of IEEE Systems, Man and Cybernetics Society

Roles on external bodies

Editorship
Member of Technical Committee for Professional Organisations
Member of Program Committees for International Conferences Shangming has served as the invited member of Program Committees for over 110 international conferences.
Teaching

Teaching

Teaching interests

Shangming’s teaching interests focused on the following areas: 
  • Machine Learning for Healthcare 
  • Health Data Analytics
  • Health Statitics
  • Health Informatics & Digital Health
  • Research Methods and Ethics
Machine Learning and Artificial Intelligence for Healthcare (MATH516)
Advanced Concepts in Research: Methodology and Methods (APP758)
MSc Dissertation and Research Skills ( PROJ518)

Staff serving as external examiners

External Examiner of Postgraduate Programmes
  • MSc Intelligent Systems, De Montfort University, UK

  • MSc Business Intelligence and Data Mining, De Montfort University, UK
  • MSc Intelligent Systems and Robotics, De Montfort University, UK
  • MSc Data Analytics, De Montfort University, UK
External Examiner of PhD Theses and Viva
  • University of Canberra, Australia
  • University of Manchester, UK
  • De Montfort University, UK
Research

Research

Research interests

Shangming’s research interests focus on AI and statistics in health and biomedical informatics: data-driven health-related studies using techniques, such as machine learning/deep learning, natural language processing, computational intelligence (artificial neural networks, fuzzy logic, nature-inspired computing etc), statistical analytics, and data mining.
He is particularly interested in trustworthy and responsible medical/health AI systems, such as explainable/transparent machine learning (deep learning) and AI for electronic health records and biomedical data analytics, and creation of innovative methods to extract personally useful information, such as rules and patterns, concerning lifestyles and health conditions from routine health related data to promote healthier lifestyles and prevent disease.
The medical conditions to which he is particularly interested in applying AI techniques include, but are not limited to, the chronic disease (such as cancer, dementia, asthma, diabetes, multiple sclerosis, mental health conditions etc.)
His areas of expertise:
  •  Artificial intelligence in health & care 
  •  Machine learning /deep learning for health data analytics
  • Health informatics
  • Explainable AI
  • Epidemiology
  • Population health
  • Big data analytics
  • Medical statistics
  • Data linkage (of electronic health records)
  •  Information aggregation/integration 
  • Biomedical signal processing
  • Data mining and knowledge discovery
  • Computational intelligence

Research degrees awarded to supervised students

  • Bruce Burnett (PhD student) (2018~ 2022) (Funded by European Convergence programme-KESS (Knowledge Economy Skills Scholarships)) - “Novel machine learning and text mining techniques for accurate disease phenotyping from SNOMED derived clinical texts”  (First supervisor) (2018~ 2020)
  • Jamie Duell (PhD student) (2019~ 2023) (Funded by EPSRC) - “Enhancing the Safe and Effective Use of Medicines in Hospitalized Patients: An AI Led Population Health Study”  (First supervisor: 2019~ 2020)
  • Elisabetta Longo (MRes student) (2019) (Funded by Gianesini Research Scholarship from UniCredit Foundation and University of Verona (Italy)) - “Patterns of polypharmacy in patients with dementia: A data-driven population-based study with primary care electronic health records” (“Best Dissertation Award in MSc Health Data Science”) (First supervisor)
  • Zahra A. Almowil (PhD student) (2018~ 2022) (Funded by Kuwait Government) - “Development of electronic health data quality assessment framework: Towards consistent measurement of data quality within electronic health records” (First supervisor: 2018~ 2020)
  • Gavin Tsang (PhD student) (2016~ 2020) (Funded by Faculty PhD studentship) - “Unravelling polypharmacy: Mining the complex interaction patterns between medications for enhanced patient care” (Second supervisor)

Grants & contracts

Dr Zhou has received research funding from different sources, including:
  • “Older people with intellectual disabilities and epilepsy – recognising and correcting anticholinergic inducing polypharmacy” (Baily Thomas Fund)
    "    Early recognition and Assessment of Severely Ill babiEs by paRents – EASIER study" (The Lullaby Trust).
  • "Using machine learning to predict subclone evolution and response during chemotherapy" (Health & Care Research Wales)
  •  "Improving Medication Verification for Cancer Patients: A Pragmatic AI Driven Population Health Study" (Faculty International PhD Studentship) 
  • "Mobilising the use of health data science into chemotherapy for cancer patients" (Higher Education Innovation Fund) 
  • "Feasibility Study - Artificial Intelligence Applied to Enhance the Safe and Effective Use of Medicines in Patients with Cancer" (Above & Beyond).
  •  "AccelerateAI – Accelerating AI research with General Purpose Graphics Processing Units" (Ser Cymru Capacity Building Accelerator Award ).
  • "UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing" (EPSRC).
  • "Health Data Research UK Wales and Northern Ireland Site" (MRC).
  • "National Centre for Population Health and Wellbeing Research" (Health and Care Research Wales).
  • "MytHICAL-Mental Health Informatics in Children, Adolescents and Young Adults: How Do my feelings become numbers?"(MRC).
  • "Study on Big Data Mining Analysis Technologies and the ASEAN Countries' Social, Economic, and Health Relations" (Guangxi University) 
  • "Novel machine learning and text mining techniques for accurate disease phenotyping from SNOMED derived clinical texts" (European Convergence Programme)
Publications

Publications

Journals
Books
Chapters
  • Omobolanle Omisade, Alex Gegov, Shang-Ming Zhou, Alice Good, Sandeep Singh Sengar, Bangli Liu, Taiwo Adedeji and Carrie Toptan, “Explainable Artificial Intelligence and Mobile Health for Treating Eating Disorders in Young Adults with Autism Spectrum Disorder based on the Theory of Change: A mixed method protocol,” Chater 3, in Vikrant Bhateja, Fina Carroll, Joao Manuel, R. S. Tavres, Sandeep Sigh Sengar, Peter Peer (ed) Intelligent Data Engineering and Analytics: Proceedings of 11th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2023), April 11 - 12, 2023, Cardiff, UK; Springer Verlag GmbH, Jan 2024 (“Best Paper Award”) (ISBN: 978-981-9967-05-6). 
  • S.-M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, “Fuzzification of the OWA Operators for aggregating uncertain information with uncertain weights,” In: Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice. J. Kacprzyk, R. Yager and G. Beliakov (editors), Studies in Fuzziness and Soft Computing, Springer Verlag, 2011, vol. 265, pp. 91-109 (invited).
  • D. Elizondo,   S.-M. Zhouand C. Chrysostomou, “Surface reconstruction techniques using neural networks to recover noisy 3D scenes,” in V. Kurkova et al (ed), Lecture Notes in Computer Science, vol.5163, Artificial Neural Networks - ICANN 2008, pp. 857-866, Springer, (ISBN: 978-3-540-87535-2).
  • J. Q. Gan and S.-M. Zhou, “A new fuzzy membership function with applications in interpretability improvement of neurofuzzy models”, Lecture Notes in Computer Science, 2006, vol. 4114, Computational Intelligence, pp. 183-194, Springer (ISBN 3-540-37274-1).
  •   S.-M. Zhou and J. Q. Gan, “Multiple objectives learning for constructing interpretable Takagi-Sugeno fuzzy model”, in Y. Jin (ed), Multi-Objective Machine Learning, Studies in Computational Intelligence, Vol.16, 2006 Springer-Verlag, pp.385-403. (Invited, ISBN: 3-540-30676-5).
Scholarly Editions
Conference Papers
Presentations and posters
  • Xu Wang, Edward Meinert, Andrea Preston, Shang-Ming Zhou. "Identification of Influential Factors in Bladder Cancer: A Co-Designed Study by Utilizing Epidemiology and Machine Learning Framework on Large Electronic Health Records Cohort." Healthcare Text Analytics Conference, Manchester, June 14-16, 2023.
  • B. Burnett, R. Lyons, P. Davies, S.-M. Zhou,“Identifying and Confirming a Colorectal Cancer Cohort in SAIL”, Medical School PGR Conference, 4~5 June 2020, Swansea, UK.
  • J. Duell, G. Aarts, S.-M. Zhou,“Machine Learning methods to determine predictors for adverse treatments in Electronic Health Records”, Medical School PGR Conference, 4~5 June 2020, Swansea, UK.
  • A. Holborow, B. Coupe, M. Davies, S.-M. Zhou,“Machine learning methods in predicting chemotherapy-induced neutropenia in oncology patients using clinical data,” Royal College of Physicians (RCP) Annual Conference, Medicine 2019, 25–26 April, 2019, Manchester Central, UK. (“Best Poster Prize”).
  • H. Raza,  S.-M. Zhou,G. Stratton, R. Hill, R. Lyons, S. Brophy. “Predictive factors associated with intensity of physical activity of 12 month infants in Environment of Healthy Living Cohort Study.”, In: Studies in Health Technology and Informatics, IOS Press: Informatics for Health 2017 / 24 – 26 April 2017 Manchester Central, UK.
  • Zhou, S.-M., Rahman, MA, Brophy, S., “Identifying predictive factors associated with outcomes of campylobacter infections from primary care electronic health records: A machine learning approach,” The Public Health England Research and Applied Epidemiology Scientific Conference2017, Warwick University, 21~22 March 2017, UK.
  • Zhou S.-M.,Rahman, MA, Lyons, RA, Brophy, S., “Data-driven drug safety signal detection methods in pharmacovigilance using electronic primary care records: A population based study,” The 2016 International Population Data Linkage Conference, 24 – 26 August, 2016, Swansea, UK.
  • Zhou,  S.-M.,  Rahman, MA, Lyons, RA, Brophy, S., “Detecting adverse drug events from routine electronic health records: A data-driven approach,” The UKCRC Public Health Research Centres of Excellence Conference, July 14-15, 2016, Norwich, UK.
  • J. I. Kennedy, F. Fernández-Gutiérrez, S.-M. Zhou, R. Cooksey, M. Atkinson, S. Brophy, “Identifying important risk factors for phenotyping patients with arthropathy conditions from high dimensional imbalanced routine data.” The Farr Institute International Conference on Health Informatics, 26th August 2015 - Friday 28th August 2015, St Andrews, UK.
  • ZhouS.-M.,Hill R, Morgan K, Stratton G, Lyons RA, Bijlsma G, Brophy S, “Establishing accelerometer wear and non-wear time events for child physical activity,” DECIPHer Research Symposium, 9th June 2015, Swansea. (“Best Poster Prize”).
  • S. Brophy, S.-M. Zhou,R. Hill, K. Morgan, G. Stratton, R. A. Lyons, G. Bijlsma, “Estimating accelerometer wear and non-wear events: comparative study of physical activity between children and adults,” The 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement. June 17-19, 2013, Amherst, Massachusetts, USA.
  • S.-M. Zhou, S. Brophy, R. Lyons, M. B. Gravenor, “A data mining study for implications of health and socioeconomic inequalities on childhood education: why care about differential impacts across geographic areas?,” The UKCRC Public Health Research Centres of Excellence 3rd Annual Conference and Summer School, Durham, UK 5~6 July 2012.
  • S.-M. Zhou, R. Lyons, S. Brophy and M. B. Gravenor. “A novel Takagi-Sugeno rule system for analysing patterns in complex epidemiological data of area based childhood deprivation indices and educational achievement,” MRC Population Health Methods and Challenges Conference, 24th~26th April, 2012, Birmingham, UK.
  • S. Brophy, R. Hill, S.-M. Zhou and R. Lyons, “MIA: Measuring infant activity – the pilot study,” MRC Population Health Methods and Challenges Conference, 24th~26th April, 2012, Birmingham, UK.
  • S.-M. Zhou, R. A. Lyons, J. C. Demmler, M. Hyatt, M. D. Atkinson, S. Paranjothy, M. B. Gravenor, “Investigation of data mining methods and epidemiological models in examining the relationship between childhood health maternal health, socioeconomic status and educational achievement,” International Conference on Exploiting Existing Data For Health Research, 9-11 September 2011, St Andrews, Scotland.
  • S.-M. Zhou, R. A. Lyons, J. C. Demmler, M. Hyatt, M. D. Atkinson, S. Paranjothy, M. B. Gravenor, “Data mining methods and epidemiological models in examining the childhood health maternal health, socioeconomic determinants of educational achievement: an analytical perspective,” Welsh Public Health Conference on Fairer Health Outcomes for All, 21st September 2011, Cardiff, Wales.
Personal

Personal

Reports & invited lectures

  • “Empowering Digital Health with Advanced Analytics : Type-1 OWA Operators for Aggregating Uncertain Information from Multiple Sources in Integrated Diagnoses.” The International Conference on Digital Health and Telemedicine 19th - 20th October 2023 (Keynote speaker). 
  • “UoP-Torbay Health Technology Showcase”, Torbay and South Devon NHS Foundation Trust, University of Plymouth, 23 May 2022.
  • “Aggregating Uncertain Information from Multiple Sources for Integrated Diagnoses”, School of Engineering, Computing and Mathematics, University of Plymouth, 18 May 2022.
  • “Artificial Intelligence in Health and Care: Promises and Challenges”, Faculty of Health, University of Plymouth, 14 September 2021.
  • “Do AI and Machine Learning Approaches Provide an Opportunity for Preventative Health and Are the Results and Predictive Capacities Reliable and Trustworthy?”, Public Debate, University of Plymouth, 23 April June 2021 (Keynote speaker). 
  • “Machine Learning and Health Data Analytics”, AI, Machine Learning and Advanced Computing Seminars, UKRI CDT, 10 June 2020.
  • “Machine Learning and Natural Language Processing with Electronic Health Records”, AI and Robotics Symposium, Cardiff University, 27 June 2019 (Keynote speaker). 
  • “Harnessing the Power of Machine Learning in Health Data Science: Prediction of the Hospitalisation of Dementia Patients from High-Dimensional Electronic Health Records”, Faculty of Biology, Medicine and Health, the University of Manchester, 8 January 2019 
  • “Mining electronic health records to identify influential predictors associated with hospitalisation of dementia patients: An artificial intelligence approach.” Lancet Public Health Conference, Belfast, 23 November 2018. 
  • “Artificial Intelligence in Healthcare: Issues, Challenges and Opportunity”, International Centre of Swansea University, UK; Sichuan Tourism University, China, 15 June 2018. 
  • “Big Data Analytics in Healthcare: Opportunity and Challenges”, International Centre of Swansea University, UK; Shenyang Aerospace University, China; 22 August 2017. 
  • “Machine Learning Techniques to Identify and Evaluate Interactive Risk Factors from Complex Epidemiological Data”, International Symposium on Embracing the Internet of Things to Data-Driven Decisions, Manchester 10~11 June 2016 (Keynote speaker). 
  • “Local System Modelling Technique: Quantifying Micro-effect of Domain Factors in Complex Interactions of Epidemiological Data”, International Conference on Engineering and Medical Informatics, Liverpool, 23~24 May 2013 (Keynote speaker).

Conferences organised

Shangming has been the chair/co-chair/co-organisier to organise the following conferences or special sessions: 
  • Special Session: “Advances on eXplainable Artificial Intelligence” for the 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021).
  • Pre-conference Workshop on Machine Learning for the 2016 International Population Data Linkage Conference (IPDLN2016).
  • Special Session: “Towards Intelligent Computing for Complex and Big Data Analysis in e-Health,” for 2014 IEEE World Congress on Computational Intelligence (WCCI 2014).
  • Special Session: “Healthcare and Enterprise Systems,” for 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013).
  • Co-chair of Program Committee for 2013 World Congress on Intelligent Systems (GCIS 2013).
  • Publication Chair for the 3rd World Congress on Intelligent Systems, 2012.
  • Special Session: “Computational Intelligence and Cyber-infrastructure for Data Mining and Complex System Modelling in Medical Informatics and e-Health”, for 2010 World Congress on Computational Intelligence (WCCI).
  • Chair of Program Committee for 2009 World Congress on Intelligent Systems (GCIS 2009)
  • Co-chair of Program Committee for 2009 World Congress on Software Engineering (WCSE 2009)
  • Special Session: “Approaches to Managing Linguistic Information in Soft Decision Making: Theory and Applications,” for 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008).
  • Special Session-“Swarm Intelligence III”, for 2008 IEEE International Congress on Evolutionary Computation (CEC).

Other academic activities

Shangming has been the editor of Special Issues for the following international journals
  • Special Issue – “Reviews on Artificial Intelligence and Natural Language Processing in Medical Diagnostics”, Diagnostics (ISSN 2075-4418), 2022 (Lead guest editor)
  • Special Issue – “Personalized Medicine with Biomedical and Health Informatics”, Journal of Personalized Medicine (ISSN 2075-4426), 2022 (Lead guest editor)
  • Special Issue – “Intelligent Data Analysis for Medical Diagnosis”, Diagnostics (ISSN 2075-4418), 2022 (Lead guest editor)
  • Special Issue – “The Role of Ontologies and Knowledge in Explainable AI”, Semantic Web journal, 2022 (Guest editor)
  • Special Issue – “Fuzzy Systems and Computational Intelligence for BioMedical Data Analysis”, Frontiers in Artificial Intelligence: Fuzzy Systems, 2020 (Associate editor)
  • Special Issue – “Decision Making in the Big Data Environment”, Frontiers in Artificial Intelligence: Fuzzy Systems, 2019 (Associate editor)
  • Special Issue – “Big data analytics in healthcare”, IEEE Transactions on Industrial Informatics, 2018(Lead associate editor)
  • Special Issue – “Advances in IoT Research and Applications”, Information Systems Frontiers, 2015, volume 17, issue 2 (Associate Editor, ISSN: 1387-3326).
  • Special Issue – “IoT-The Internet of Things in Industry”, IEEE Transactions on Industrial Informatics vol.10, no.2, 2014. (Associate Editor, ISSN: 1551-3203).
  • Special Issue – “Enterprise Information Systems with Industrial Applications”, IEEE Transactions on Industrial Informatics, vol.8, no.3, 2012. (Associate Editor, ISSN: 1551-3203).
  • Special Issue – “Integrated Healthcare Information Systems”, IEEE Transactions on Information Technology in Biomedicine, vol.16, no.4, 2012 (Associate Editor, ISSN: 1089-7771).
  • Special Issue – “User Centered Health Informatics”, International Journal of Healthcare Technology and Management, vol.13, no.5/6, 2012. (Guest Editor, ISSN: 1368-2156).