Shangming Zhou

Address:

Centre for Health Technology, Faculty of Health
University of Plymouth, Plymouth, UK

Research Interests:

• Big Data Analytics & Data Science in Healthcare
• Big Data & Real-World Evidence Generation
• Electronic Health Records (EHR) Analytics & Data Linkage
• Artificial Intelligence (AI) in Healthcare
• Explainable AI (XAI) & Ethical AI
• Machine Learning & Deep Learning for Health Data
• Natural Language Processing (NLP) for Clinical Text
• Disease Phenotyping & Risk Prediction
• Epidemiology & Population Health
• Digital Health & e-Health Transformation

Biography:

Dr. Shang-Ming Zhou is Professor of e-Health and Health Data Science at the University of Plymouth, UK, with extensive experience in AI in healthcare, electronic health records analytics, health data science, and biomedical statistics. He currently serves as Deputy Director of the Centre for Health Technology, Director of NHS Kernow DataLab, and is an Affiliated Investigator with Health Data Research UK. His research is supported by funders such as HDR UK, MRC, EPSRC, HCRW, and international collaborators.
 

Dr. Zhou’s work spans explainable machine learning (XAI), ethical AI in healthcare, natural language processing for health, disease phenotyping, and multimorbidity & polypharmacy studies. He is especially interested in using AI and big data to generate real-world evidence from electronic health records, driving personalised medicine and improving patient safety. He has supervised multiple PhD students in projects like AI-driven prediction models for disease prognosis, dietetics, and cancer patient care.
 

Outside research, Dr. Zhou contributes to teaching in Machine Learning, Health Informatics, and Ethics; serves on editorial boards of several leading journals; and has held leadership roles on professional committees. He is committed to advancing data-driven healthcare solutions that are transparent, ethical, and impactful.
 

Achievements:

Prof. Shang-Ming Zhou has received multiple international recognitions for his contributions to health data science and artificial intelligence in healthcare.

• Recipient of the Springer Nature Best Paper Award at the International Conference on Frontiers of Intelligent Computing.
• Winner of the Best Poster Prize at the Royal College of Physicians Annual Conference.
• Honored with the IFIP-WG8.9 Outstanding Academic Service Award.
• Awarded Outstanding Reviewer recognitions from leading journals, including Journal of Biomedical Informatics, IEEE Transactions on Cybernetics, Applied Soft Computing, Knowledge-Based Systems, and Expert Systems with Applications.
• Serves on editorial boards of respected international journals such as Frontiers in Artificial Intelligence, Scientific Reports, Diagnostics, Journal of Personalized Medicine, and Frontiers in Neurology.

Current Research Projects:

Prof. Zhou is actively involved in several cutting-edge projects that combine big data analytics, machine learning, and healthcare innovation:

• Machine learning-based prediction of endometrial cancer prognosis – PhD supervision (2023–2025).
• AI-led population health study for medication verification in cancer patients – research direction (2022–2025).
• Explainable AI for multimorbidity and polypharmacy using large-scale electronic health records – HDR UK collaborations.
• Artificial Intelligence in Nursing Education – advancing digital health training and simulation (2023–2029).
• Big Data analytics and AI for tuberculosis detection using HeroRats – supervisory role (2023–2027).

Academic Profiles of Dr. Shang-Ming Zhou

Explore his academic and professional presence across trusted platforms:

• University of Plymouth Profile
• Google Scholar
• ORCID
• ResearchGate
• LinkedIn
• Scopus
• Web of Science

Publications:

Dr. Shang-Ming Zhou has an extensive record of research in health informatics, Artificial Intelligence (AI) in healthcare, big data analytics, and population health. His contributions focus on explainable machine learning (XAI), electronic health records (EHR) integration, and digital health transformation. With a strong publication and citation record, his work addresses key challenges in early disease detection, multimorbidity, and patient safety. The publications below highlight his most recent and impactful research in advancing AI-driven healthcare innovation.

 

1. Zhou, S.M., et al. (2025). AI-driven biomedical perspectives on mental fatigue in the post-COVID-19 Era: trends, research gaps, and future directions. Journal of Big Data, 12:112.
2. Zhou, S.M., et al. (2025). Advancing AI Literacy in Medical Education: A Medical AI Competency Framework Development. In: Lecture Notes in Computer Science, Springer, AIED 2025.
3. Xia, X.L., Zhou, S.M., Liu, Y., Lin, N., & Overton, I.M. (2025). ieGENES: A machine learning method for selecting differentially expressed genes in cancer studies. Journal of Biomedical Informatics, 152, 104803.
4. Omobolanie Omisade, G. A., Zhou, S.M., Good, A., Tryfona, C., Sengar, S.S., Prior, A.M., Liu, B., Adedeji, T., & Toptan, C. (2023). 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. In: AI Applications in Healthcare, Springer.
5. Zhou, S.M., McLean, B., Roberts, E., Baines, R., Hannon, P., Ashby, S., Newman, C., Sen, A., Wilkinson, E., Laugharne, R., et al. (2023). Analysing patient-generated data to understand behaviours and characteristics of women with epilepsy of childbearing years: A prospective cohort study. Seizure – European Journal of Epilepsy, 108, 33–42.

 

 

Last Updated on September 12, 2025