Professor Shang-Ming Zhou
Profiles

Professor Shang-Ming Zhou

Professor of e-Health

School of Nursing and Midwifery (Faculty of Health)

Professor Shang-Ming Zhou works with the Centre for Health Technology in the Faculty of Health: Medicine, Dentistry and Human Sciences. He is also an affiliated investigator with the Health Data Research UK (HDR UK). His research was funded by HDRUK, MRC, EPSRC, HCRW, charity, and international collaborations.

Before joining the University of Plymouth, 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 scholarly interests focus on AI and statistics in health and biomedical informatics: data-driven health-related studies using techniques, such as machine learning/deep learning, natural language processing, computational intelligence (artificial neural networks, fuzzy logic, nature-inspired computing etc), statistical analytics, and data mining.

He was the recipient of “Best Poster Prize” at the Royal College of Physicians (RCP) Annual Conference; IFIP-WG8.9 “Outstanding Academic Service Award"; and “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.

Prospective PhD students

Shangming is very interested in supervising strong potential PhD students in the area of AI and machine learning (/deep learning) in healthcare, health data analytics, mining electronic health records, population/public health etc. If you have an ambition in pursuing a PhD study in these areas, you are most welcome to contact him via email in the above.

Call for Papers

        Submission Deadlines: 15 June 2021 - Abstract; 15 July 2021 - Manuscript

  • Special Session : “Advances on eXplainable Artificial Intelligence” for the 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), Jul. 11 - 14, 2021; Luxembourg (Organized by: Jose M. Alonso, Ciro Castiello, Corrado Mencar, Luis Magdalena, Shang-Ming Zhou)
       Deadlines: Paper submission - February 10th, 2021 ; Acceptance/rejection notification - March 22nd, 2021

Qualifications

BSc, MSc, PhD, FHEA

Professional membership

  • Fellow of the Higher Education Academy
  • Member of IEEE (The Institute of Electrical and Electronics Engineers)
  • Member of IEEE Engineering in Medicine & Biology Society
  • Member of IEEE Computational Intelligence Society
  • Member of IEEE Systems, Man and Cybernetics Society

Roles on external bodies

Editorship and Member of Editorial Boards

Member of Technical Committee for Professional Organisations Member of Program Committees for International Conferences

Shangming has served as the invited member of Program Committees for over 110 international conferences, such as some recent ones :

  • 2020 The 16th International Conference on Data Science (ICDATA'20)
  • 2020 The 15th International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2020)
  • 2020 The 16th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020)
  • 2020 The 9th International Congress on Advanced Applied Informatics (IIAI AAI 2020)
  • 2020 The 5th International Conference on Enterprise Architecture and Information Systems (EAIS 2020)

Teaching interests

Shangming’s teaching interests focused on the following areas:

  • Machine Learning for Healthcare
  • Health Data Analytics
  • Health Statitics
  • Health Informatics & Digital Health
  • Research Methods and Ethics

Staff serving as external examiners

External Examiner of Postgraduate Programmes

  • MSc Intelligent Systems, De Montfort University, UK
  • MSc Business Intelligence and Data Mining, De Montfort University, UK
  •  MSc Intelligent Systems and Robotics, De Montfort University, UK
  • MSc Data Analytics, De Montfort University, UK

 External Examiner of PhD Theses and Viva

  • University of Canberra, Australia
  • University of Manchester, UK
  • De Montfort University, UK

Research interests

Shangming’s research interests focus on AI and statistics in health and biomedical informatics: data-driven health-related studies using techniques, such as machine learning/deep learning, natural language processing, computational intelligence (artificial neural networks, fuzzy logic, nature-inspired computing etc), statistical analytics, and data mining.

He is particularly interested in trustworthy and responsible medical/health AI systems, such as explainable/transparent machine learning (deep learning) and AI for electronic health records and biomedical data analytics, and creation of innovative methods to extract 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.

The medical conditions to which he is particularly interested in applying AI techniques include, but are not limited to, the chronic disease (such as cancer, dementia, asthma, diabetes, multiple sclerosis, mental health conditions etc.)

His areas of expertise:

  • Health informatics
  • Health data analytics
  • Epidemiology
  • Population health
  • Medical statistics
  • Data linkage (of electronic health records)
  • Information aggregation/integration
  • Biomedical signal processing
  • Artificial intelligence in healthcare
  • Machine learning (deep learning)
  • Big data analytics
  • Data mining and knowledge discovery
  • Computational intelligence

Grants & contracts

Dr Zhou has received research funding from different sources, including:

  • Ser Cymru Capacity Building Accelerator Award: AccelerateAI – Accelerating AI research with General Purpose Graphics Processing Units
  • Above and Beyond: Feasibility Study - Artificial Intelligence Applied to Enhance the Safe and Effective Use of Medicines in Patients with Cancer
  • EPSRC: Enhancing the Safe and Effective Use of Medicines in Hospitalized Patients: An AI Led Population Health Study
  • EPSRC: UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing
  • MRC: Health Data Research UK Wales and Northern Ireland Site
  • Health and Care Research Wales: National Centre for Population Health and Wellbeing Research
  • MRC: MytHICAL-Mental Health Informatics in Children, Adolescents and Young Adults: How Do my feelings become numbers?
  • Guangxi University: Study on Big Data Mining Analysis Technologies and the ASEAN Countries' Social, Economic, and Health Relations
  • European Convergence Programme : Novel machine learning and text mining techniques for accurate disease phenotyping from SNOMED derived clinical texts

Journals

S.-M. Zhou, F. Chiclana, R. I. John, J. M. Garibaldi, and L. Huo, “Type-1 OWA Operators in Aggregating Multiple Sources of Uncertain Information: Properties and Real World Application in Integrated Diagnosis,” IEEE Transactions on Fuzzy Systems, 2021 (https://doi.org/10.1109/TFUZZ.2020.2992909).

L. Huo, L. Bai, S.-M. Zhou‡, “Automatically Generating Natural Language Descriptions of Images by A Deep Hierarchical Framework.” IEEE Transactions on Cybernetics, 2021 (‡Corresponding author) (https://doi.org/10.1109/TCYB.2020.3041595).

G. Tsang, S.-M. Zhou, and X. Xie, "Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1-13, 2021 (https://doi.org/10.1109/JTEHM.2020.3040236).

Tsang, G., Xie, X., Zhou, S.-M. ‡, , “Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges.” IEEE Reviews in Biomedical Engineering, 13: 113-129; 2020 (https://doi.org/10.1109/RBME.2019.2904488) (‡Corresponding author)

E. Longo, L. Huo, J. Demmler, A. Morris, S. Brophy, R. A. Lyons, S.-M. Zhou, “Patterns of polypharmacy in patients with dementia: A data-driven population-based study with primary care electronic health records”, Lancet, Volume 394, Special Issue(S67), 2019 (https://doi.org/10.1016/S0140-6736(19)32864-8) (‡Corresponding author).

H. Raza, S.-M. Zhou‡ , C. Todd, D. Christian, E. Merchant, K. Morgan, A. Khanom, R. Hill, R. Lyons, S. Brophy. “Predictors of Objectively Measured Physical Activity in 12-months Old Infants: An Environment of Healthy Living Cohort Study.” Pediatric Obesity, 2019: e12512 (https://doi.org/10.1111/ijpo.12512) (‡Corresponding author)

H. Raza, D. Rathee, S.-M. Zhou‡ , H. Cecotti, and G. Prasad, “Covariate shift detection and adaptation based ensemble learning for handling inter-or-intra session non-stationarity in EEG-based brain-computer interface,” Neurocomputing, vol.343, pp.154–16, 2019 (https://doi.org/10.1016/j.neucom.2018.04.087) (‡Corresponding author)

A. Holborow, B. Coupe, M. Davies, S.-M. Zhou, “Machine learning methods in predicting chemotherapy-induced neutropenia in oncology patients using clinical data,” Clinical Medicine. 2019 Jun; 19(Suppl 3): 89–90 (https://dx.doi.org/10.7861%2Fclinmedicine.19-3s-s89)

S.-M. Zhou, Gavin Tsang, Xianghua Xie, Lin Huo, Sinead Brophy, and Ronan A Lyons. “Mining electronic health records to identify influential predictors associated with hospitalisation of dementia patients: An artificial intelligence approach.” Lancet, Vol.392, Special Issue(S9), November, 2018 (https://doi.org/10.1016/S0140-6736(18)32166-4)

S.-M. Zhou, R. A. Muhammad, S. Sheppard, R. Howe, R. A. Lyons, S. Brophy. “Mining electronic health records for identification of predictive factors associated with hospitalisation of Campylobacter infections.” Lancet, Vol.390, Special Issue(S99), 2017 (http://dx.doi.org/10.1016/S0140-6736(17)33034-9)

H. Raza, S.-M. Zhou, R. Hill, R. Lyons, S. Brophy. “Identification of predictors of objectively measured physical activity in 12-month-old British infants: A machine learning driven study.” Lancet, Vol.390, Special Issue(S74), 2017 (http://dx.doi.org/10.1016/S0140-6736(17)33009-X)

X.-L. Xia, S. Brophy, S.-M. Zhou. “Learning differentially expressed gene pairs in microarray data,” Stud Health Technol Inform. 2017;235:191-195 (http://dx.doi.org/10.3233/978-1-61499-753-5-191) (‡Corresponding author)

D. A. Elizondo, S.-M. Zhou, and C. Chrysostomou, “Light source detection for digital images in noisy scenes: A neural network approach,” Neural Computing and Applications, vol. 28, issue 5, pp. 899-909, May 2017 (https://doi.org/10.1007/s00521-016-2281-0).

S.-M. Zhou, F. Fernandez-Gutierrez, J. Kennedy, R. Cooksey, M. Atkinson, S. Denaxas, S. Siebert, W. G. Dixon, T. W. O’Neill, E. Choy, C. Sudlow, UK Biobank Follow-up and Outcomes Group, S. Brophy, “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), 2016: e0154515 (http://dx.doi.org/10.1371/journal.pone.0154515)(ISSN: 1932-6203, PLoS).

Zhou, S.-M. , Hill, R.A., Morgan, K., Stratton, G., Gravenor, M. B., Bijlsma, G., Brophy, S., “Classification of accelerometer wear and non-wear events in seconds for monitoring free living physical activity,” BMJ Open, 2015;5:e007447 (http://dx.doi.org/10.1136/bmjopen-2014-007447) (ISSN: 2044-6055, BMJ).

Zhou, S.-M., Lyons R.A., Bodger O., John A., Brunt H., Jones K.H., Gravenor M.B., Brophy S. “Local modelling techniques for assessing micro-level impacts of risk factors in complex data: Understanding health and socioeconomic inequalities in childhood educational attainments,” PLoS ONE 9(11): e113592, 2014 (http://dx.doi.org/10.1371/journal.pone.0113592) (ISSN: 1932-6203, PLoS).

K. Morgan, M. Rahman, R. Hill, S.-M. Zhou, G. Bijlsma, A. Khanom, R. Lyons, and S. Brophy. “Physical activity and excess weight in pregnancy have independent and unique effects on delivery and perinatal outcomes”. PLoS ONE 9(4): e94532, 2014 (http://dx.doi.org/10.1371/journal.pone.0094532) (ISSN: 1932-6203, PLoS).

F. Mata, L. G. Perez, S.-M. Zhou, F. Chiclana, "Type-1 OWA methodology to consensus reaching processes in group decision making with multi-granular linguistic contexts," Knowledge-based Systems, vol.58, pp.11-22, 2014 (http://dx.doi.org/10.1016/j.knosys.2013.09.017) (ISSN: 0950-7051, Elsevier Science Inc.).

F. Chiclana, S.-M. Zhou, “Type-reduction of general type-2 fuzzy sets: The type-1 OWA method,” International Journal of Intelligent Systems, vol. 28, pp.505–522, 2013 (http://dx.doi.org/10.1002/int.21588) (ISSN: 08848173, John Wiley & Sons).

K. Morgan, M. Rahman, R. Hill, M. Atkinson, S.-M. Zhou, A. Khanom, R. Lyons, S. Paranjothy and S. Brophy. “Association of diabetes in pregnancy with child weight at birth, age 12 months and 5 years – a population-based electronic cohort study.” PLoS One 8 (11), e79803, 2013 (http://dx.doi.org/10.1371/journal.pone.0079803) (ISSN: 1932-6203, PLoS).

Brophy S, Cooksey R, Davies H, Dennis M S, Zhou, S.-M., Siebert S.,The effect of physical activity and motivation on function in ankylosing spondylitis: a cohort study,” Seminars in Arthritis and Rheumatism, 42(6):619-26, June 2013 (http://dx.doi.org/10.1016/j.semarthrit.2012.09.007) (ISSN: 0049-0172, Elsevier).

Brophy S, Jones KH, Rahman MA, Zhou, SM., John A, Atkinson M, Francis N, Lyons RA, Dunstan F, “Incidence of Campylobacter and Salmonella infections following first prescription for PPI– a cohort study using routine data,” American Journal of Gastroenterology, vol.108, no.7, pp.1094-1100, 2013 (http://dx.doi.org/10.1038/ajg.2013.30) (ISSN: 0002-9270, NPG).

Zhou, S.-M., Lyons RA, Brophy S, Gravenor MB, “Constructing compact Takagi-Sugeno rule systems: Identification of complex interactions in epidemiological data,” PLoS ONE 7(12): e51468, 2012 (http://dx.doi.org/10.1371/journal.pone.0051468) (ISSN: 1932-6203, PLoS).

Li, L., Ge, R.-L., Zhou, S.-M., Valerdi, R., “Integrated healthcare information systems,” IEEE Transactions on Information Technology in Biomedicine, vol.16, no.4, 2012, pp. 515-517 (http://dx.doi.org/10.1109/TITB.2012.2198317) (ISSN: 1089-7771, IEEE).

J. M. Garibaldi, S.-M. Zhou, X.-Y. Wang, R. I. John, I. O. Ellis, “Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models,” Journal of Biomedical Informatics, Vol 45, No 3, pp.447–459, June 2012 (http://dx.doi.org/10.1016/j.jbi.2011.12.007). (ISSN: 1532-0464, Elsevier) (‡Corresponding author)

Brophy S, Cooksey R, Atkinson M, Zhou, S.-M., Husain M.J, Macey S, Rahman M, Siebert S., "No increased rate of acute myocardial infarction or stroke among patients with Ankylosing Spondylitis – a retrospective cohort study using routine data," Seminars in Arthritis and Rheumatism,42(2):140-145, Oct 2012. (http://dx.doi.org/10.1016/j.semarthrit.2012.02.008) (ISSN: 0049-0172, Elsevier).  

W. Ma, D. Tran, H. Lin, S.-M. Zhou, B. Oh, G. Waddington, D. Sharma, M. A Rahman, O. Sirisaengtaksin, J. Scarvell and T. McGrath, “Information technology challenges and opportunities in personal healthcare systems,” International Journal of Healthcare Technology and Management, 13(5/6):345-362, 2012 (http://dx.doi.org/10.1504/IJHTM.2012.052564) (ISSN: 1368-2156, Inderscience).

S.-M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, “Alpha-level aggregation: a practical approach to type-1 OWA operation for aggregating uncertain information with applications to breast cancer treatments,” IEEE Transactions on Knowledge and Data Engineering, Vol.23, No.10, pp. 1455-1468, 2011. (http://dx.doi.org/10.1109/TKDE.2010.191) (ISSN: 10414347, IEEE).

S.-M. Zhou, R. I. John, F. Chiclana and J. M. Garibaldi, “On aggregating uncertain information by type-2 OWA operators for soft decision making,” International Journal of Intelligent Systems, Vol. 25, No.6, pp. 540-558, 2010. (http://dx.doi.org/10.1002/int.20420 ) (ISSN: 08848173, John Wiley & Sons).

W. Pan, L. Xu, S.-M. Zhou, Z. Fan, Y. Li, and F. Shan, “A novel Bayesian learning method for information aggregation in modular neural networks,” Expert Systems with Applications, Vol.37, No.2, pp. 1071-1074, 2010. (http://dx.doi.org/10.1016/j.eswa.2009.06.104) (ISSN: 0957-4174, Elsevier Science).

S.-M. Zhou and J. Q. Gan, “Extracting Takagi-Sugeno fuzzy rules with interpretable submodels via regularization of linguistic modifiers,” IEEE Transactions on Knowledge and Data Engineering, Vol.21, No.8, pp. 1191-1204, August 2009. (http://dx.doi.org/10.1109/TKDE.2008.208) (ISSN: 10414347, IEEE).

S.-M. Zhou, J. M. Garibaldi, R. I. John and F. Chiclana, “On constructing parsimonious type-2 fuzzy logic systems via influential rule selection,” IEEE Transactions on Fuzzy Systems, Vol.17, No.3, pp.654-667, June 2009. (http://dx.doi.org/10.1109/TFUZZ.2008.928597) (ISSN: 1063-6706, IEEE).

S.-M. Zhou, J. Q. Gan, L. -D. Xu and R. I. John, “Fuzziness index-driven fuzzy relaxation and applications to image processing," Annals of Operations Research, Vol.168, Nol.1, pp.119-131, 2009. (http://dx.doi.org/10.1007/s10479-008-0363-9) (ISSN: 02545330, Springer).

S.-M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, “Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers,” Fuzzy Sets and Systems, Vol.159, No.24, pp.3281-3296, December, 2008. (http://dx.doi.org/10.1016/j.fss.2008.06.018) (ISSN: 0165-0114, Elsevier Science Inc).

R. I. John, S.-M. Zhou, J. M. Garibaldi and F. Chiclana, “Automated group decision making support systems under uncertainty: trends and future research,” International Journal of Computational Intelligence Research, Vol.4, No.4, pp.357–371, 2008 (ISSN: 0974-1259, Research India Publications).

S.-M. Zhou and J. Q. Gan, “Low-level interpretability and high-level interpretability: a unified view of interpretable fuzzy system modelling from data,” Fuzzy Sets and Systems, Vol.159, No.23, pp.3091-3131, December 2008. (http://dx.doi.org/10.1016/j.fss.2008.05.016) (ISSN: 0165-0114, Elsevier Science Inc).

S.-M. Zhou, J. Q. Gan and F. Sepulveda, “Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface,” Information Sciences, Vol.178, No.6, pp.1629-1640, March 2008 (http://dx.doi.org/10.1016/j.ins.2007.11.012) (ISSN: 0020-0255, Elsevier Science Inc.).

S.-M. Zhou and J. Q. Gan, “Constructing L2-SVM-based fuzzy classifiers in high-dimensional space with automatic model selection and fuzzy rule ranking,” IEEE Transactions on Fuzzy Systems, Vol. 15, No. 3, pp.398~409, June 2007. (http://dx.doi.org/10.1109/TFUZZ.2006.882464) (ISSN: 1063-6706, IEEE).

S.-M. Zhou, J. Q. Gan, L.-D. Xu and R. John, “Interactive image enhancement by fuzzy relaxation,” International Journal of Automation and Computing, Vol. 4, No. 3, pp. 229 ~ 235, 2007. (http://dx.doi.org/10.1007/s11633-007-0229-7) (ISSN: 1476-8186, Springer).

S.-M. Zhou and J. Q. Gan, “Constructing accurate and parsimonious fuzzy models with distinguishable fuzzy sets based on an entropy measure,” Fuzzy Sets and Systems, Vol. 157, No. 8, pp. 1057-1074, April 2006. (http://dx.doi.org/10.1016/j.fss.2005.08.004) (ISSN: 0165-0114, Elsevier Science Inc).

S.-M. Zhou and J. Q. Gan, “A new fuzzy relaxation algorithm for image enhancement,” International Journal of Knowledge-Based & Intelligent Engineering Systems, Vol. 10, No. 3, pp.181-192, 2006. (http://iospress.metapress.com/content/8b4kku7kqf2par45/) (ISSN:1327-2314, IOS Press).

S.-M. Zhou and J. Q. Gan, “An unsupervised kernel based fuzzy c-means clustering algorithm with kernel normalization,” International Journal of Computational Intelligence and Applications, Vol. 4, No.4, pp.355-373, 2004. (http://dx.doi.org/10.1142/S1469026804001379) (ISSN: 1469-0268, World Scientific).

S.-M. Zhou H. -X. Li and L. -D. Xu, “A variational approach to intensity approximation for remote sensing images using dynamic neural network,” Expert Systems, Vol.20, No.4, pp.163-170, 2003. (http://onlinelibrary.wiley.com/doi/10.1111/1468-0394.00240/pdf) (ISSN: 0266-4720, Blackwell).

J. -L. Li, S.-M. Zhou and C. -D. Dai, “Methods of automatically computing land area of blocks in classified remote sensing images,” Remote Sensing Technology and Application, Vol.17, No.3, pp.158-161, 2002. (ISSN: 1004-0323)

S.-M. Zhou and L. -D. Xu, “A new type of recurrent fuzzy neural network for modeling dynamical system,” Knowledge-Based Systems, Vol.14, No.5-6, pp.243-251, August 2001. (http://dx.doi.org/10.1016/S0950-7051(01)00102-2) (ISSN: 0950-7051, Elsevier Science Inc.).

S.-M. Zhou, “Combining dynamic neural network and image sequences in a dynamic model for complex industrial production processes,” Expert Systems with Applications, Vol.16, pp.13-19, January 1999. (http://dx.doi.org/10.1016/S0957-4174(98)00023-2) (ISSN: 0957-4174, Elsevier Science).

Books

S.-M. Zhou and W. Wang, Proceedings of 2009 Global Congress on Intelligent Systems, IEEE Computer Society, May 2009 (ISBN:978-0-7695-3571-5). (http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5209216)

D. Tran and S.-M. Zhou, Proceedings of 2009 World Congress on Software Engineering, IEEE Computer Society, May 2009 (ISBN:978-0-7695-3570-8). (http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5319633)

Chapters

S.-M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, “Fuzzification of the OWA Operators for aggregating uncertain information with uncertain weights,” In: Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice. J. Kacprzyk, R. Yager and G. Beliakov (editors), Studies in Fuzziness and Soft Computing, Springer Verlag, 2011, vol. 265, pp. 91-109 (http://dx.doi.org/10.1007/978-3-642-17910-5_5 ) (invited).

D. Elizondo, S.-M. Zhou and C. Chrysostomou, “Surface reconstruction techniques using neural networks to recover noisy 3D scenes,” in V. Kurkova et al (ed), Lecture Notes in Computer Science, vol.5163, Artificial Neural Networks - ICANN 2008, pp. 857-866, Springer, (http://dx.doi.org/10.1007/978-3-540-87536-9_88) (ISBN: 978-3-540-87535-2).

J. Q. Gan and S.-M. Zhou, “A new fuzzy membership function with applications in interpretability improvement of neurofuzzy models”, Lecture Notes in Computer Science, 2006, vol. 4114, Computational Intelligence, pp. 183-194, Springer (ISBN 3-540-37274-1).

S.-M. Zhou and J. Q. Gan, “Multiple objectives learning for constructing interpretable Takagi-Sugeno fuzzy model”, in Y. Jin (ed), Multi-Objective Machine Learning, Studies in Computational Intelligence, Vol.16, 2006 Springer-Verlag, pp.385-403. (Invited, ISBN: 3-540-30676-5) (http://dx.doi.org/10.1007/3-540-33019-4_17)

Scholarly Editions

Shang-Ming Zhou, Le Hoang Son, Arunangsu Chatterjee,Wai Lok Woo, “Fuzzy Systems and Computational Intelligence for BioMedical Data Analysis”, Frontiers in Artificial Intelligence: Fuzzy Systems, 2021

P. Wang, R. Valerdi, S.-M. Zhou, L. Li , “Advances in IoT research and applications”, Information Systems Frontiers (2015) 17(2), April 2015.

Zhou, Z., Valerdi, R., Zhou, S.-M., Wang, L., “IoT-The Internet of Things in Industry”, IEEE Transactions on Industrial Informatics, vol.10, no.2, 2014.

L. Li, R.-L. Ge, S.-M. Zhou, R. Valerdi, “Integrated Healthcare Information Systems”, IEEE Transactions on Information Technology in Biomedicine, vol.16, no.4, 2012. July 2012

Zhou, Z., Valerdi, R., Zhou, S.-M.,  “Enterprise Information Systems with Industrial Applications”, IEEE Transactions on Industrial Informatics, vol.8, no.3, 2012. August 2012.

Hissam Tawfik, Shang-Ming Zhou, “User Centred Health Informatics”, International Journal of Healthcare Technology and Management, vol.13, no.5/6, 2012.

Conference Papers

F. Fernández-Gutiérrez, J. I. Kennedy, S.-M. Zhou, R. Cooksey, M. Atkinson, and S. Brophy. “Comparing feature selection methods for high-dimensional imbalanced data: identifying rheumatoid arthritis cohorts from routine data.” Proceedings of International Conference on Industrial Engineering and Systems Management, 21-23 October, 2015, Seville, Spain (DOI: 10.1109/IESM.2015.7380164).

S.-M. Zhou, M. A. Rahman, M. A. Atkinson, S. Brophy, “Mining textual data from primary healthcare records - Automatic identification of patient phenotype cohorts,” Proceedings of 2014 IEEE Joint International Conference on Neural Networks, 6~11 July, 2014, Beijing, China, pp. 3621 – 3627 (DOI: 10.1109/IJCNN.2014.6889494, ISBN: 978-1-4799-6627-1)

J. Deng, X. Xie, and S.-M. Zhou, “Conversational interaction recognition based on bodily and facial movement,” Proc. of International Conference on Image Analysis and Recognition, October 22-24, 2014, Vilamoura, Algarve, Portugal, pp. 237-245 (ISBN: 978-3-319-11757-7) (http://dx.doi.org/10.1007/978-3-319-11758-4_26 )

W. Ma, D. Tran, T. Pham, T. Le, H. Lin, S.-M. Zhou, “Using EEG artifacts for BCI applications,” Proceedings of 2014 IEEE Joint International Conference on Neural Networks, 6~11 July, 2014, Beijing, China, pp. 3628-3635 (DOI: 10.1109/IJCNN.2014.6889496).

F. Mata, F. Chiclana, S.-M. Zhou, "Type-1 OWA based multi-granular consensus model," Spanish Conference on Fuzzy Logic (ESTYLF), Valladolid (Spain), 1~3 February 2012, pp. 235-240 (ISBN: 978-84-615-6653-2)

F. Chiclana, S.-M. Zhou, “The type-1 OWA operator and the centroid of type-2 fuzzy sets,” Proceedings of The 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011), 18-22 July 2011, Aix-Les-Bains, France, pp. 15-20 (http://dx.doi.org/10.2991/eusflat.2011.138)

S.-M. Zhou, R. A. Lyons, O. Bodger, J. C. Demmler and M. A. Atkinson, “SVM with entropy regularization and particle swarm optimization for identifying children’s health and socioeconomic determinants of education attainments using linked datasets,” Proceedings of 2010 IEEE Joint International Conference on Neural Networks, 18~23 July, 2010, Barcelona, Spain, pp.3867-3874. (https://doi.org/10.1109/IJCNN.2010.5596973 ) (ISBN: 978-1-4244-6917-8)

S.-M. Zhou, R. A. Lyons and M. B. Gravenor, “Construction of parsimonious Takagi-Sugeno fuzzy rule based model in characterising impacts of child health, socioeconomic deprivations on educational outcomes,” Proceedings of 2010 IEEE International Conference on Fuzzy Systems, 18~23 July, 2010, Barcelona, Spain, pp.999-1006. (https://doi.org/10.1109/FUZZY.2010.5583937 ) (ISBN: 978-1-4244-6920-8).

S.-M. Zhou, J. M. Garibaldi, F. Chiclana, R. I. John and X.-Y. Wang, “Type-1 OWA operator based non-stationary fuzzy decision support systems for breast cancer treatments”, Proc. of 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Jeju Island, Korea, 20-24 August, 2009, pp.175-180 (https://doi.org/10.1109/FUZZY.2009.5277142 ) (ISBN: 978-1-4244-3597-5).

X.-Y. Wang, J. M. Garibaldi, S.-M. Zhou, and R. I. John, “Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer”, Proc. of 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Jeju Island, Korea, 20-24 August, 2009 pp.1187-1192 (https://doi.org/10.1109/FUZZY.2009.5277077 ) (ISBN: 978-1-4244-3597-5).

S.-M. Zhou, R. I. John, X. -Y. Wang, J. M. Garibaldi and I. O. Ellis, "Compact fuzzy rules induction and feature extraction using SVM with particle swarms for breast cancer treatments," Proc. of 2008 IEEE Congress on Evolutionary Computation (CEC), 1-6 June, 2008, Hong Kong, China, pp.1469-1475 (https://doi.org/10.1109/CEC.2008.4630987 ) (ISBN: 978-1-4244-1823-7).

S.-M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, “Type-2 OWA operators -aggregating type-2 fuzzy sets in soft decision making,” Proc. of 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6 June, 2008, Hong Kong, China, pp.625-630 (https://doi.org/10.1109/FUZZY.2008.4630434 ) (ISBN: 978-1-4244-1819-0).

S.-M. Zhou, and F. Chiclana, “Inducing linguistic weights by type-2 quantifiers for type-1 OWA operators in soft decision making,” Proc. of 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6 June, 2008, Hong Kong, China, pp.1626-1633 (https://doi.org/10.1109/FUZZY.2008.4630589 ) (ISBN: 978-1-4244-1819-0).

S.-M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, “On properties of type-1 OWA operators in aggregating fuzzy sets for soft decision making,” Proc. of the 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU), 22-27 June, 2008, Malaga, Spain, pp.1368-1375 (ISBN: 978-84-612-3061-7).

S.-M. Zhou, F. Chiclana, R. I. John and J. M. Garibaldi, “A practical approach to type-1 OWA operation for soft decision making,” Proc. of the 8th International FLINS Conference on Computational Intelligence and Control, 21-24 September, 2008, Madrid, Spain, pp.507-512 (ISBN: 981279946X).

S. G. Matthews, S. Coupland and S.-M. Zhou, "An integrated stereo vision and fuzzy logic controller for following vehicles in an unstructured environment," Proc. of 2008 UK Workshop on Computational Intelligence (UKCI), 10-12 September, 2008, Leicester, UK.

S.-M. Zhou, R. John, F. Chiclana and J. Garibaldi, “New type-2 rule ranking indices for designing parsimonious interval type-2 fuzzy logic systems,” Proc. of the 2007 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2007), London, UK, 23 - 26 July, 2007, pp. 853-858 (https://doi.org/10.1109/FUZZY.2007.4295477 ) (ISBN: 1-4244-1210-2).

W. Blewitt, S.-M. Zhou, and S. Coupland, “A novel approach to type-2 fuzzy addition,” Proc. of the 2007 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2007), London, UK, 23 - 26 July, 2007, pp. 1456 – 1461 (https://doi.org/10.1109/FUZZY.2007.4295581) (ISBN: 1-4244-1210-2).

S.-M. Zhou, F. Chiclana, R. John and J. M. Garibaldi, “Type-1 OWA operators for aggregating fuzzy sets in decision making,” Proc. of EUSFLAT AGOP, 9-14 July, 2007, Ghent, Belgium, pp. 107-112, Academia Press (ISBN:978-90-382-1140-4).

S.-M. Zhou and Q. Gan, “L2-SVM based fuzzy classifiers with automatic model selection and fuzzy rule ranking,” Proc. of the UK Workshop on Computational Intelligence (UKCI), London, 5-7 September, 2005, UK, pp.75-82.

S.-M. Zhou, J. Q. Gan and F. Sepulveda, “Using higher-order statistics from EEG signals for developing brain-computer interface (BCI) systems,” Proc. of the UK Workshop on Computational Intelligence (UKCI), 6-8 September, 2004, Loughborough University, UK. (ISBN:1-874152-11-X)

S.-M. Zhou, and J. Q. Gan, “Interpretability improvement of input space partitioning by merging fuzzy sets based on an entropy measure”, Proc. of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 25-29 July, 2004, Budapest, Hungary, pp.287-292. (https://doi.org/10.1109/FUZZY.2004.1375736 ) (ISBN:0-7803-8354-0)

S.-M. Zhou and J. Q. Gan, “Improving the interpretability of Takagi-Sugeno fuzzy model by using linguistic modifiers and a multiple objective learning scheme,” Proc. of International Joint Conference on Neural Networks (IJCNN), 25-29 July, 2004, Budapest, Hungary, pp.2385-2390. (https://doi.org/10.1109/IJCNN.2004.1381001) (ISBN: 0-7803-8360-5)

S.-M. Zhou and J. Q. Gan, “Mercer kernel, fuzzy c-means algorithm and prototypes of clusters,” in Yang et al (eds.):Proc. of the 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'04), 25-27 August, 2004, Exeter, UK, Lecturer Notes in Computer Science Vol. 3177, pp.613-618, Springer-Verlag 2004. ( ISBN: 3-540-22881-0)

S.-M. Zhou and Q. Gan, “A new fuzzy relaxation algorithm for image contrast enhancement,” Proc. of IEEE 3rd International Symposium on Image and Signal Processing and Analysis, Vol.1, pp.11-16, 18-20 September, 2003, Rome, Italy. (https://doi.org/10.1109/ISPA.2003.1296859 ) (ISBN: 953184063-6)

S.-M. Zhou and Q. Gan, “New fuzzy data clustering based on information entropy measure and relaxation linguistic modifier,” Proc. of The UK Workshop on Computational Intelligence(UKCI), Bristol, 1-3 September, 2003, UK, pp.141-148. (ISBN: 0862925371)

S.-M. Zhou, “A new approach to fuzzy modeling based on recurrent neural network for fuzzy dynamic systems,” Proc. of the 14th World Congress of International Federation of Automatic Control (IFAC), 5-9 July, 1999, Beijing, China, Elsevier Science, Vol. K, pp. 39 - 44. (ISBN: 0-08-043248-4)

Posters

B. Burnett, R. Lyons, P. Davies, S.-M. Zhou, “Identifying and Confirming a Colorectal Cancer Cohort in SAIL”, Medical School PGR Conference, 4~5 June 2020, Swansea, UK.

J. Duell, G. Aarts, S.-M. Zhou, “Machine Learning methods to determine predictors for adverse treatments in Electronic Health Records”, Medical School PGR Conference, 4~5 June 2020, Swansea, UK.

A. Holborow, B. Coupe, M. Davies, S.-M. Zhou, “Machine learning methods in predicting chemotherapy-induced neutropenia in oncology patients using clinical data,” Royal College of Physicians (RCP) Annual Conference, Medicine 2019, 25–26 April, 2019, Manchester Central, UK. (Best Poster Prize).

H. Raza, S.-M. Zhou, G. Stratton, R. Hill, R. Lyons, S. Brophy. “Predictive factors associated with intensity of physical activity of 12 month infants in Environment of Healthy Living Cohort Study.”, In: Studies in Health Technology and Informatics, IOS Press: Informatics for Health 2017 / 24 – 26 April 2017 Manchester Central, UK.

Zhou, S.-M.,  Rahman, MA, Brophy, S., “Identifying predictive factors associated with outcomes of campylobacter infections from primary care electronic health records: A machine learning approach,” The Public Health England Research and Applied Epidemiology Scientific Conference 2017, Warwick University, 21~22 March 2017, UK.

Zhou, S.-M., Rahman, MA, Lyons, RA, Brophy, S., “Data-driven drug safety signal detection methods in pharmacovigilance using electronic primary care records: A population based study,” The 2016 International Population Data Linkage Conference, 24 – 26 August, 2016, Swansea, UK.

Fernandez-Gutierrez, F., Kennedy, J., Cooksey, R., Atkinson, M., Brophy, S., Zhou, S.-M., “Development of data-driven framework for automatically identifying patient cohorts from linked electronic health records,” International Journal of Population Data Science (2017) Issue 1, Vol 1:067 (http://dx.doi.org/10.23889/ijpds.v1i1.86).

Zhou, S.-M., Rahman, MA, Lyons, RA, Brophy, S., “Detecting adverse drug events from routine electronic health records: A data-driven approach,” The UKCRC Public Health Research Centres of Excellence Conference, July 14-15, 2016, Norwich, UK.

J. I. Kennedy, F. Fernández-Gutiérrez, S.-M. Zhou, R. Cooksey, M. Atkinson, S. Brophy, “Identifying important risk factors for phenotyping patients with arthropathy conditions from high dimensional imbalanced routine data.” The Farr Institute International Conference on Health Informatics, 26th August 2015 - Friday 28th August 2015, St Andrews, UK

Zhou S.-M., Hill R, Morgan K, Stratton G, Lyons RA, Bijlsma G, Brophy S, “Establishing accelerometer wear and non-wear time events for child physical activity,” DECIPHer Research Symposium, 9th June 2015, Swansea. (Best Poster Prize)

S. Brophy, S.-M. Zhou, R. Hill, K. Morgan, G. Stratton, R. A. Lyons, G. Bijlsma, “Estimating accelerometer wear and non-wear events: comparative study of physical activity between children and adults,” The 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement. June 17-19, 2013, Amherst, Massachusetts, USA.

S.-M. Zhou, S. Brophy, R. Lyons, M. B. Gravenor, “A data mining study for implications of health and socioeconomic inequalities on childhood education: why care about differential impacts across geographic areas?,” The UKCRC Public Health Research Centres of Excellence 3rd Annual Conference and Summer School, Durham, UK 5~6 July 2012.

S.-M. Zhou, R. Lyons, S. Brophy and M. B. Gravenor. “A novel Takagi-Sugeno rule system for analysing patterns in complex epidemiological data of area based childhood deprivation indices and educational achievement,” MRC Population Health Methods and Challenges Conference, 24th~26th April, 2012, Birmingham, UK.

S. Brophy, R. Hill, S.-M. Zhou, and R. Lyons, “MIA: Measuring infant activity – the pilot study,” MRC Population Health Methods and Challenges Conference, 24th~26th April, 2012, Birmingham, UK.

S.-M. Zhou, R. A. Lyons, J. C. Demmler, M. Hyatt, M. D. Atkinson, S. Paranjothy, M. B. Gravenor, “Investigation of data mining methods and epidemiological models in examining the relationship between childhood health maternal health, socioeconomic status and educational achievement,” International Conference on Exploiting Existing Data For Health Research, 9-11 September 2011, St Andrews, Scotland.

S.-M. Zhou, R. A. Lyons, J. C. Demmler, M. Hyatt, M. D. Atkinson, S. Paranjothy, M. B. Gravenor, “Data mining methods and epidemiological models in examining the childhood health maternal health, socioeconomic determinants of educational achievement: an analytical perspective,” Welsh Public Health Conference on Fairer Health Outcomes for All, 21st September 2011, Cardiff, Wales.


Reports & invited lectures

  • Machine Learning and Natural Language Processing with Electronic Health Records”, AI and Robotics Symposium, Cardiff University, 27 June 2019
  • Harnessing the Power of Machine Learning in Health Data Science: Prediction of the Hospitalisation of Dementia Patients from High-Dimensional Electronic Health Records”, Faculty of Biology, Medicine and Health, the University of Manchester, 8 January 2019
  • “Mining electronic health records to identify influential predictors associated with hospitalisation of dementia patients: An artificial intelligence approach.” Lancet Public Health Conference, Belfast, 23 November 2018
  • Artificial Intelligence in Healthcare: Issues, Challenges and Opportunity”, International Centre of Swansea University, UK; Sichuan Tourism University, China, 15 June 2018
  • Big Data Analytics in Healthcare: Opportunity and Challenges”, International Centre of Swansea University, UK; Shenyang Aerospace University, China; 22 August 2017
  • Machine Learning Techniques to Identify and Evaluate Interactive Risk Factors from Complex Epidemiological Data”, International Symposium on Embracing the Internet of Things to Data-Driven Decisions, Manchester 10~11 June 2016

Other academic activities

Shangming has been the chair/co-chair/co-organisier to organise the following conferences or special sessions:
  • Special Session: “Advances on eXplainable Artificial Intelligence” for the 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021).
  • Pre-conference Workshop on Machine Learning for the 2016 International Population Data Linkage Conference (IPDLN2016).
  • Special Session: “Towards Intelligent Computing for Complex and Big Data Analysis in e-Health,” for 2014 IEEE World Congress on Computational Intelligence (WCCI 2014).
  • Special Session: “Healthcare and Enterprise Systems,” for 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013).
  • Co-chair of Program Committee for 2013 World Congress on Intelligent Systems (GCIS 2013).
  • Publication Chair for the 3rd World Congress on Intelligent Systems, 2012.
  • Special Session: “Computational Intelligence and Cyber-infrastructure for Data Mining and Complex System Modelling in Medical Informatics and e-Health”, for 2010 World Congress on Computational Intelligence (WCCI).
  • Chair of Program Committee for 2009 World Congress on Intelligent Systems (GCIS 2009)
  • Co-chair of Program Committee for 2009 World Congress on Software Engineering (WCSE 2009)
  • Special Session: “Approaches to Managing Linguistic Information in Soft Decision Making: Theory and Applications,” for 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008).
  • Special Session-“Swarm Intelligence III”, for 2008 IEEE International Congress on Evolutionary Computation (CEC).