Professor Shangming Zhou PhD
University of Plymouth, Devon · United Kingdom
Editorial leadership for Journal of Big Data Research ISSN 2768-0207
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 Professor. Shangming Zhou
Explore his academic and professional presence across trusted platforms:
Selected publications
- Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface 2019 cited 94×
- Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges 2020 cited 48×
- Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review 2023 cited 24×
- Automatically Generating Natural Language Descriptions of Images by a Deep Hierarchical Framework 2022 cited 11×
- Mining Primary Care Electronic Health Records for Automatic Disease Phenotyping: A Transparent Machine Learning Framework 2021 cited 10×
- Analysing patient-generated data to understand behaviours and characteristics of women with epilepsy of childbearing years: A prospective cohort study 2023 cited 9×
Ranked by citation impact (Crossref) where available, newest otherwise · verified via ORCID.
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This journal is guided by Professor Shangming Zhou (University of Plymouth, Devon) and a peer-review board of practising researchers. Open access, author-retained copyright (CC BY), and a clear editorial process.