Senior Lecturer
Telephone: (01792) 602580
Room: Cellular Office - 202
Second Floor
Data Science Building
Singleton Campus

Dr Zhou’s research sits within the College “Patient & Population Health and Informatics (PPHI)"Theme. His scholarly interests focus on health and biomedical informatics: data-driven health-related studies by using computing techniques, such as medical statistics, machine learning, computational intelligence (deep learning, fuzzy logic, nature-inspired computing etc), predictive analytics and data mining. He is particularly interested in intelligent data analytics of electronic health records and –omics data, 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.

  • Dr Zhou is the Fellow of The Higher Education Academy.
  • 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".
  • 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).

 Prospective PhD students

Dr Zhou is very interested to supervise strong potential PhD students in the following areas. If you are interested in pursuing your PhD studies in any of these areas, or other problems in the area of healthcare machine learning and informatics, predictive analytics, mining electronic health records etc.,  you are welcome to contact Dr Zhou y email at the above address.

1) Machine Learning for Pharmacoepidemiological Surveillance
2) Data mining research on detecting adverse drug events for pharmacovigilance
3) Building evidence-base on multimorbidity and polypharmacy in primary care: Data-driven study
4) Machine learning for disease phenotyping from routine electronic healthcare records
5) Identifying determinants of health outcomes from routine electronic healthcare records
6) Identification of complex interactions of risk factors in epidemiological data 
7) Intelligent data analytics for integrative –omics and electronic health records for precise disease understanding.
8) Causal reasoning and modelling in epidemiology and public health.
9) Biomedical signal processing and applications:

  • Physical activity and sedentary behaviour analysis;
  • EEG signals for charactering state of a patient's health.

Areas of Expertise

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


  1. & Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface. Neurocomputing
  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(5), e0154515
  3. & Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity. BMJ Open 5(5), e007447-e007447.
  4. & (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,
  5. & Introduction: Advances in IoT research and applications. Information Systems Frontiers 17(2), 239-241.

See more...


  • 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. This is a core/compulsory module of 20 credits. Module leaders are Arron Lacey and Shangming Zhou


  • Unravelling Polypharmacy: Determining interaction patterns between medications using complex electronic health records for better patient care. (current)

    Student name:
    Other supervisor: Dr Shang-Ming Zhou
    Other supervisor: Dr Xianghua Xie
  • Mining free-text clinical notes for early prediction of the progressions and recurrences of colorectal cancer. (current)

    Student name:
    Other supervisor: Prof Ronan Lyons
    Other supervisor: Dr Shang-Ming Zhou
  • Development and Evaluation of a Secure Data Portal and Interactive Platform of Disease Phenotyping for Retrieval and Analysis of the Terminologies of Diagnoses, Symptoms, Medications and Procedures in Wales (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.