Rate My Professor Weizi Li

Professor Weizi (Vicky) Li is a Professor of Informatics and Digital Health and Deputy Director of the Informatics Research Centre at Henley Business School, University of Reading. She is a Fellow of the Chartered Institute of IT (British Computer Society). An interdisciplinary researcher, she focuses on artificial intelligence and machine learning, information systems, digital health, advanced analytics, decision support systems, digital leadership, and strategy. Her research applies informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges, including early disease detection, personalised prediction, integrated clinical pathways, and data-driven improvements in healthcare quality using real-world data from electronic patient records, remote monitoring, and patient-reported outcomes.

Prof Li is Programme Director for MSc Digital and Technology Solutions and MSc Informatics (BIT). She directs the EPSRC Future Blood Testing for Inclusive Monitoring and Personalised Analytics Network+ (EP/W000652/1, 2021-2024) and leads projects funded by NIHR, ESRC, EPSRC, The Health Foundation, NHS, and Innovate UK. Notable initiatives include the Integrated Clinical Pathway Management and Cloud-based Digital Data Integration Platform (ESRC O2RB Excellence in Impact Award 2018; 4*/3* REF 2021 impact case study) and a machine learning decision support system for appointment attendance at Royal Berkshire NHS Foundation Trust (University of Reading Research Engagement and Impact Award 2020; shortlisted for 2022 impact award, HSJ patient safety award, THE STEM award 2025). She received a £600,000 UKRI grant for AI to predict inflammatory arthritis (2023) and led a £1 million EPSRC blood testing network (2021). Key publications encompass Wang et al. (2023) 'Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning' (Decision Support Systems); Chan et al. (2023) 'Time in patterns: machine learning based blood glucose fluctuation pattern recognition for Type 1 diabetes management' (JMIR AI); Dashtban and Li (2022) 'Predicting non-attendance in hospital outpatient appointments using Deep Learning Approach' (Health Systems); and recent works on explainable machine learning for psoriatic arthritis flares and axial spondyloarthritis (2025-2026). Her contributions have resulted in NHS-implemented systems tackling health inequalities and enhancing patient care.