Dr Zhiqiang Huo

Profile

I am a Lecturer in Health Data Science at Queen Mary’s Population Precision Health at Centre for Primary Care, within Clinical Effectiveness Group (CEG).

I lead research focused on developing novel AI-driven methods that leverage large-scale electronic health records (EHRs) to support clinical decision-making in GP practices, with the aim of improving patient outcomes and population health in North East London. To support this, I closely work in a multidisciplinary team - including clinicians, digital health teams, and health system leaders - to ensure that our research delivers real-world impact.

Prior to joining Queen Mary, I worked as a Research Associate at University College London (UCL) and King’s College London (KCL), where I co-led the development of AI-supported early warning tools to enhance clinical decision-making in paediatric intensive care, and co-produced digital self-management platforms that empower stroke survivors to actively monitor their health and reduce the risk of secondary events.

I bring inter-disciplinary expertise in Health Data Science, Health Informatics and Computer Science, with a focus on advancing clinical AI and digital health innovation. My work also integrates these technical strengths with stakeholder-led co-production for improved clinical decision-making.

I provide supervision to undergraduate and postgraduate students (MSc, PhD, postdoc). I welcome students who are passionate about applying data science to real-world healthcare challenges and enjoy working across disciplines (health data science and computer science) in a collaborative and inclusive team.

Outside of work, I am an active member of the British Academy Early Career Researcher Network and the IEEE Industrial Electronics Society’s Technical Committee on Cloud and Wireless Systems for Industrial Applications. I have also contributed to the academic community by serving on guesting editors for international journals and programme committees of over eight international conferences.

Find more information about Zhiqiang Huo from personal website.

Research

Research Interests:

I am interested in developing AI-driven methods that harness large-scale electronic health records to support clinical decision-making and improve patient outcomes. My work explores time series analysis, machine learning, and signal processing for real-world healthcare applications. I am also interested in the co-production of digital health tools with clinicians and patients to ensure usability, impact, and adoption in practice.

Publications

Huo, Z., Booth, J., Monks, T. et al (2025). Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI. npj Digital Medicine. 8, 108. https://doi.org/10.1038/s41746-025-01465-w

Huo, Z., Booth, J., Monks, T. et al (2023). Distribution and trajectory of vital signs from high-frequency continuous monitoring during pediatric critical care transport. Intensive Care Medicine – Paediatric and Neonatal (ICMpn) 1, 18. https://doi.org/10.1007/s44253-023-00018-x 

Huo, Z., Neate, T., Wyatt, D., Rowland-Coomber, S., Chapman, M., Marshall, I. J., ... & Curcin, V. (2024, June). Co-Designing a User-Centred Digital Portal to Support Health-Related Self-Management for Stroke Survivors. In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI) (pp. 418-425). (Best Paper Award). https://doi.org/10.1109/ICHI61247.2024.00060

Huo, Z., Martínez-García, M., Zhang, Y., & Shu, L. (2021). A multisensor information fusion method for high-reliability fault diagnosis of rotating machinery. IEEE Transactions on Instrumentation and Measurement, 71, 1-12. https://doi.org/10.1109/TIM.2021.3132051

Huo, Z., Martínez-García, M., Zhang, Y., Yan, R., & Shu, L. (2020). Entropy measures in machine fault diagnosis: Insights and applications. IEEE Transactions on Instrumentation and Measurement, 69(6), 2607-2620. https://doi.org/10.1109/TIM.2020.2981220

More publications can be found through Google Scholar.