Dr Zhou’s research sits within the College “Patient & Population Health and Informatics (PPHI)"Theme. His scholarly interests focus on data-driven approaches to epidemiology and public health problems using computing techniques, such as medical statistics, machine learning, computational intelligence, predictive analytics and data mining. He is particularly interested in intelligent data analysis of complex health records, and creation of innovative methods for extracting 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. His on-going research topics include, but are not limited by

1) Identification of complex interactions of risk factors in epidemiological data.

2) Data driven approach to healthcare modelling and analytics using big health-related data.

3) Causal reasoning and modelling in epidemiology and public health.

4) Biomedical signal processing and applications:
(1) Physical activity and sedentary behaviour analysis
(2) EEG signals for charactering state of a patient's health.

5) Identifying adverse events in inpatients/outpatients and pharmacovigilance.

6) Identifying patient phenotype cohorts from electronic healthcare records

Dr Zhou sits on the IEEE Systems, Man and Cybernetics (SMC) Technical Committee on Enterprise Information Systems, and International Federation for Information Processing (IFIP) Technical Committee on Information Systems -WG 8.9. He was the recipient of IFIP-WG8.9 “Outstanding Academic Service Award" (2007).

Dr Zhou is the Associate Editor for “Journal of Intelligent & Fuzzy Systems” (ISSN : 1064-1246), and is a member of Editorial Board for “World Journal of Methodology” (ISSN: 1949-8462).

Areas of Expertise

  • Health informatics
  • Big data analytics
  • Epidemiology and public health
  • Medical statistics
  • Machine learning
  • Data mining and knowledge discovery
  • Fuzzy logic and modeling
  • Biomedical signal processing
  • Information aggregation
  • Computational intelligence


  1. & Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity. BMJ Open 5(5), e007447-e007447.
  2. & Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis. PLOS ONE 11
  3. & (2015). Comparing feature selection methods for high dimensional imbalanced data: identifying rheumatoid arthritis cohorts from routine data. Presented at Proceedings of the 6th International Conference on Industrial Engineering and Systems Management,
  4. & Introduction: Advances in IoT research and applications. Information Systems Frontiers 17(2), 239-241.
  5. & (2014). Using EEG artifacts for BCI applications. , 3628-3635. doi:10.1109/IJCNN.2014.6889496

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  • PMIM402 Machine Learning in Healthcare

    Data scientists working in healthcare are called to deal with problems involving classification and pattern recognition. The objective of this module is to provide the essential theory and practical aspects of widely used machine learning software.


  • Untitled (current)

    Student name:
    Other supervisor: Dr Xianghua Xie
    Other supervisor: Dr Shang-Ming Zhou
  • Untitled (current)

    Student name:
    Other supervisor: Prof Sinead Brophy
    Other supervisor: Dr Shang-Ming Zhou

External Responsibilities

Research Groups

  • Patient & Population Health and Informatics (PPHI)

    PPHI is a multidisciplinary research centre of international standing, conducting eHealth and Informatics Research, Health Services Research and Population Health Studies. Its goal is to make a difference to people’s lives by substantially improving their health and the delivery of health services.

  • The Farr Institute @ CIPHER

    The CIPHER ( Centre for Improvement in Population Health through E-records Research ) is one of the four co-ordinating centres of the Farr Institute, UK, funded by a consortium of 10 UK Government and Charity Funders led by Medical Research Council (MRC). It is a multinational research partnership between academia, the UK national health service (NHS) and industry, focussed on improving the lives of patients and the population through informatics.


    The DECIPHer (Centre for the Development and Evaluation of Complex Interventions for Public Health Improvement) is one of five UKCRC Public Health Research Centres of Excellence coordinated by the Medical Research Council. The centre aims to develop, test, evaluate and implement complex interventions and policies that achieve sustainable improvements in health and wellbeing, and address health inequalities. Our research has a particular focus on the health of children and young people.

  • Welsh Arthritis Research Network (WARN)

    The Welsh Arthritis Research Network (WARN) has been established by the National Institute for Social Care and Health Research and the Welsh Government. It aims to identify arthritis and musculoskeletal research priorities for Wales, raise the profile of arthritis and musculoskeletal research in Wales and ultimately improve the quality of life and care of people living with arthritis in Wales.