Please note that this event is now online-only. 

Abstract:

Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of AI models to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a data set of 2.3 million ECGs collected from patients with 7 years follow-up, we developed a DNN model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hypertension screening solely from ECGs, which are then used to stratify the risk of mortality. Our model demonstrated cardiologist-level accuracy in interpretable cardiac diagnosis, and the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available. In addition, we explored the design of optimal DNN models through of a novel Neural Architecture Search (NAS) approach, which was able to find networks outperformed the state-of-the-art models with fewer than 5% parameters.

Bio:

Dr. Lei Lu is a Lecturer in Health Data Science and AI at King’s College London, and a Visiting Research Fellow at the Institute of Biomedical Engineering, University of Oxford. Lei obtained his PhD from the Harbin Institute of Technology in China, complemented by two-year visiting research at the University of British Columbia in Canada. Upon completing his PhD study, Lei had his postdoctoral research at the University of Melbourne in Australia. Subsequently, he joined the Institute of Biomedical Engineering at University of Oxford as a Senior Research Associate. Lei’s work focuses on clinical machine learning to advance healthcare outcomes. Lei is actively engaged in a range of academic roles, including invited speaker at the IET Annual Healthcare Lecture and the IEEE-EMBS Symposium on MDBS. He also served as conference session chair, workshop committee, and guest editor for IJCAI, CIKM, ICRA, and IEEE JBHI. He received the IET J.A. Lodge Award in 2021, which is presented annually to one early-career researcher with distinction.

Creating context around a book's main character and arc can bring to readers an enlarged understanding of their story and its importance to their time. It can also lead the writer into the weeds, causing a reader to lose interest in your work. In this talk, Prof. Michael Meyer will discuss his approach and methods whilst writing about 1870s London and the many "B characters" in 'A Dirty, Filthy Book,' which recounts Annie Besant's obscenity trial. Meyer has also written extensively about China, and will talk about about world-building for readers unfamiliar with a story's setting, history, and language.

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Please note: this event is only open to members of our Research Network, which you can join via the OCLW website.

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The Global Parenting Initiative: Research and scale-up of human-digital evidence-based playful parenting programmes in the Global South



Join us for our online inaugural event launching our 5-year plan to support the scale-up and institutionalisation of evidence-based playful parenting programmes in the Global South, and globally. We will hear from members of our global team about our key focus areas, including cutting-edge research and innovation, capacity building, and advocacy for the delivery of free, human-digital programmes for every parent, everywhere.