Shang-Ming Zhou

Academic profile

Professor Shang-Ming Zhou

Professor of e-Health
School of Nursing and Midwifery (Faculty of Health)

The Global Goals

In 2015, UN member states agreed to 17 global to end poverty, protect the planet and ensure prosperity for all. Shang-Ming's work contributes towards the following SDG(s):

Goal 03: SDG 3 - Good Health and Well-beingGoal 12: SDG 12 - Responsible Consumption and ProductionGoal 17: SDG 17 - Partnerships for the Goals

About Shang-Ming

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聽

  • AI in health and care;
  • explainable machine learning (XAI) in healthcare;聽
  • 聽ethical AI in healthcare;
  • electronic health records analytics;
  • 聽natural language processing /text mining in healthcare;
  • e-health technology transformation;
  • early detection and diagnosis;
  • multimorbidity and polypharmacy;聽
  • disease phenotyping;聽
  • patient safety;
  • 别迟肠.听

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 Deputy Director of the Centre for Health Technology at the Faculty of Health: Medicine, Dentistry and Human Sciences. He is also the Director of NHS Kernow Datalab, and an with the (HDR UK). His research was funded by HDRUK, MRC, EPSRC, HCRW, Charities, and international collaborations. Before joining the 抖阴短视频, 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 鈥搊mics 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.)

His areas of expertise:

  • 聽Artificial intelligence in health & care聽
  • 聽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)
  • 聽Information aggregation/integration聽
  • Biomedical signal processing
  • Data mining and knowledge discovery
  • Computational intelligence


He was the recipient of 鈥� Best Paper Award 鈥� sponsored by Springer Nature at the International Conference on Frontiers of Intelligent Computing: Theory and Applications; 鈥� Best Poster Priz e鈥� at the Royal College of Physicians (RCP) Annual Conference; IFIP-WG8.9 鈥� Outstanding Academic Service Award "; and 鈥� 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.

Supervised Research Degrees

PhD Students

  • Sherif Shazly (2023~2025)鈥� 鈥淢achine learning-based prediction of endometrial cancer prognosis鈥� (Director of Study) .
  • Xu Wang (2022~2025) 鈥� 鈥淚mproving Medication Verification for Cancer Patients: An AI Led Population Health Study鈥� (Director of Study) .
  • Xiatian Fan聽 (2023~2027) 鈥� 鈥淢ining Routinely Collected Electronic Health Records to Identify Effective Dietetic Factors for Optimal Care in General Practice鈥� (Director of Study) .
  • Tristan Coombe (2023~2029) 鈥� 鈥淥n the Use of Artificial Intelligence in Nursing Education鈥� (Director of Study) .
  • Joan Jonathan Mnyambo (2023~2027) 鈥掆€淧rediction of Diagnostic Accuracy using Artificial Intelligence and Big Data Analytics from HeroRats for Tuberculosis Detection鈥� (Second Supervisor)聽

Teaching

Shangming鈥檚 teaching interests focused on the following areas:聽
  • Machine Learning for Healthcare聽
  • 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)

Contact Shang-Ming

+44 1752 586513