This is the web page of the Life Sciences and Health track of the ABCP 2024 Annual Conference, to be held on Friday 5th July 2024.

Chinese beverages and snacks, such as sour plum juice, iced black tea, aloe vera juice, peanut crisp, and Laopo cake, are supplied for this Parallel Thematic Track by BICI.



Friday 5th July 2024 (Venue: Room 044A, University of Leicester School of Business, Leicester LE2 1RQ +  MS Teams / Meeting ID: 385 322 025 773 Passcode: xyEg2k)

1:30-4:45pm Parallel Thematic Tracks – Life Science and Health (Co-organized with Beijing Institute of Collaborative Innovation, BICI), Co-chaired by Huiliang Li, UCL, UK and Wen Wang, University of Leicester, UK
1:30-1:40pm Welcome Remarks by Huiliang Li and Wen Wang
1:40-2pm Introduction to BICI
Dr Xinjian Zhou, President of BICI, China
2-2:15pm Sonic Needles for Surgeons: the Translational Journey with BICI
Dr Wenfeng Xia, King’s College London, UK
2:15-2:30pm Disease Diagnosis Based on Non-Invasive Breath Analysis and MEMS Micro GC Technology
Dr Junqi Wang, Founder and CEO of ChromX Health, China
2:30-2:40pm Association of Air Pollution Exposure in Old Age with Diabetes Risk: Does It Interact with Smoking?
Prof Ruoling Chen, University of Wolverhampton, UK
2:40-2:50pm Biomarker Validation and Cell Target siRNA Intervention in AKI
Prof Bin Yang, University Hospitals of Leicester, UK
2:50-3pm Machine Learning in Skin Capacitive Imaging Analysis (Online)
Prof Perry Xiao, London South Bank University, UK
3-3:30pm Refreshment Break(Venue: Brookfield Atrium)
3:30-3:40pm Healthcare Big Data: A Showcase from PowerAI-CVD, A Chinese-specific Artificial Intelligence-powered Predictive Model for Cardiovascular Disease (Online)
Professor Gary Tse, Hong Kong Metropolitan University, China
show more/less


Professor Gary Tse, MEASA, BA (Hons. Cantab.), MBBS (Imperial), MA. (Dubl.), MD (Cantab.), DM (Oxon.), PhD (Cantab.), FFPH, FRCP, FRCPath

Routinely collected electronic health records (EHRs) data contain a vast amount of valuable information for conducting epidemiological studies. With the right tools, we can gain insights into disease processes and development, identify the best treatment and develop accurate models for predicting outcomes. Our recent systematic review has found that the number of big data studies from Hong Kong has rapidly increased since 2015, with an increasingly common application of artificial intelligence (AI). The advantages of big data are that i) the models developed are highly generalisable to the population, ii) multiple outcomes can be determined simultaneously, iii) ease of cross-validation by for model training, development and calibration, iv) huge numbers of useful variables can be analyzed, v) static and dynamic variables can be analysed, vi) non-linear and latent interactions between variables can be captured, vii) AI approaches can enhance the performance of prediction models. Our team has developed risk models for common non-communicable diseases such as cardiovascular disease (CVD) and diabetes mellitus, as well as rare congenital heart diseases of Brugada syndrome and long QT syndrome using population-based datasets.
In this presentation, we will showcase using PowerAI-CVD, a Chinese-specific artificial intelligence-driven predictive model for CVD. This will illustrate our collaborative efforts between clinicians, data scientists and statisticians in utilising multi-modality data. In the form of dashboards, unique features include real-time trends analysis and risk updates using newly accumulated data from ongoing testing. AI-driven models outperform traditional models in terms of sensitivity, specificity, accuracy, area under the receiver operating characteristic and precision-recall curve, and F1 score. Web and/or mobile versions of the risk models allow clinicians to risk stratify patients quickly in clinical settings, thereby facilitating decision-making. Efforts are required to identify the best ways of implementing AI algorithms on the web and mobile apps. In conclusion, the benefits of a big data approach are that only routinely collected EHR data are required for developing high-performance predictive models.


Prof. Gary Tse matriculated at Trinity Hall, University of Cambridge in 2005 to read pre-clinical medicine. He subsequently completed his clinical training at Imperial College London, as well as PhD and MD from the University of Cambridge. He is Associate Dean (Innovations and Research) at the School of Nursing and Health Studies, Hong Kong Metropolitan University (HKMU). He is also a Professor at the Department of Cardiology, Second Hospital of Tianjin Medical University, China and a Visiting Professor at the Faculty of Health and Medical Sciences, University of Surrey. Prior to joining HKMU, Prof. Tse held a joint position as Clinical Reader at the University of Kent (with permanent appointment until the retirement age) and Honorary Public Health Consultant at the Medway Council in local government, UK. He serves as a Nucleus Committee Member of the Population Health Section, European Association of Preventive Cardiology. He is a Fellow of the Faculty of Public Health, Royal College of Pathologists and Royal College of Physicians, and an elected Member of the European Academy of Sciences and Arts (Class II: Medicine). He has been listed on the World’s Top 2% Scientists Released by Stanford University for the Cardiovascular System & Hematology subfield since 2020 and is ranked 26th on the Top Cardiovascular Researchers from China in 2023. He has delivered more than 70 lectures as Faculty in international conferences, has an H-index of 59 and has obtained more than HK$94 million research-related funding. He leads the Hong Kong Risk Modelling Team focusing on the use of big data and artificial intelligence for cardiovascular risk prediction.

3:40-3:45pm Intelligent Biosignal Processing and Machine Learning for Prediction of Lethal Ventricular Arrhythmias using Holter ECGs
Dr Xin Li, University of Leicester, UK
show more/less


Dr Xin Li, University of Leicester

Dr Xin Li obtained BEng in Electrical Information Engineering from the University of Science and Technology Beijing 2011 and MSc in Electrical Electronic Engineering from the University of Leeds in 2012. He has been awarded his PhD in Biomedical Engineering from the University of Leicester in 2016. He was appointed as Research Associate from 2016 and promoted to Lecturer in 2019 at Departments of Cardiovascular Sciences and Engineering, University of Leicester, UK. His research focused on using advanced signal processing and mathematical intelligent algorithms for improving target identification for catheter ablation during human persistent atrial fibrillation and better risk assessment for sudden cardiac death. He serves as the associated editors for Biomedical Signal Processing and Control and reviewers of several high impact journals (IEEE Trans, Mbec, CirC EP etc.) and guest editor of Frontiers in Physiology.

3:45-3:50pm Impact of Advanced Paternal Age on Implantation Failure: Interactive Effects with Advanced Maternal Age and Paternal Smoking and Alcohol Drinking
Dr Jiaqian Yin, University of Wolverhampton, UK
3:50-3:55pm An Intersectional Study of Family Carers: Examining the Interaction between Ethnicity and Young Adulthood to Develop Appropriate Support
Chandini Subramanyam, University of Leicester, UK
show more/less


Chandini Subramanyam, University of Leicester

Chandini is a 2nd year PhD student in the Population Health Sciences department at the University of Leicester. Her research interests include global mental health, health inequity, and supporting family carers. Her PhD aims to understand how communities can better support ethnically diverse, young adult family carers. Her PhD is a collaboration between the University of Leicester and the LOROS Hospice Centre for Excellence. Chan is also the College of Life Sciences student representative and co-chairs the Student Voice committee and the College’s Race and Ethnicity committee.

3:55-4pm A Computational Approach to Predict High Speed Jet Noise Affecting Ground Crew Health
Zhihan Wang, University of Leicester, UK
4-4:05pm Natural Deer Antler Scaffold Repairs Bone Defect by Co-culture MSC and Macrophages
Jianping Zhang, UCL, UK
4:05-4:35pm Roundtable Discussion: Opportunities and Challenges of Technology Innovation
4:35-4:45pm Prize Announcement

Organising Committee

  • Professor Huiliang Li (李会良教授), University College London (UCL) & Vice-President of ABCP for Industrial Liaison & Fund Raising
  • Dr Wen Wang (汪文博士), University of Leicester, UK & Co-Chairs and Non-Voting Members of Standing Management Committee of ABCP
  • Dr Yuan Gao, University of Leicester, UK & Online Admin of Life Sciences and Health Thematic Track of ABCP 2024 Conference

How to join the sessions online

Please use the MS Teams Meeting ID: 385 322 025 773 Passcode: xyEg2k, or scan the QR code below