Kezhi Li

Lecturer

University College London

United Kingdom

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Dr. Kezhi Li is a Lecturer at University College London, UK. His research areas include federated learning, deep learning applications in health, and predictive modeling for diabetes management. Dr. Li's recent publications focus on topics such as blockchain technology in federated learning, glucose prediction models, and the application of explainable AI in clinical settings.

Articles (17)

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2025

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2021

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2021

Collaborators (7)

Sonya Crowe

Professor at Department of Mathematics

University College London

UNITED KINGDOM

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Pantelis Georgiou

Imperial College London

UNITED KINGDOM

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Zhiqiang Huo

University College London

UNITED KINGDOM

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Johan H Thygesen

Associate Professor

University College London

UNITED KINGDOM

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Chengzhe Piao

University College London

UNITED KINGDOM

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Taiyu Zhu

King's College London

UNITED KINGDOM

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Nick Oliver

UNITED KINGDOM

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Kezhi Li

Research Interests

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Articles (17)

Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI

Multi-Horizon Glucose Prediction Across Populations With Deep Domain Generalization

GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series

Game-Theoretic Incentive Mechanism for Blockchain-Based Federated Learning

Unravelling the association of glycosylated haemoglobin A1c, blood pressure, and LDL-cholesterol (ABC) with all-cause mortality in Type 2 diabetes patients: insights from a middle-income country

Large Language Models for Mental Health Applications: A Systematic Review (Preprint)

Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge

IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing

Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning

GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks

Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes

Machine learning for real-time aggregated prediction of hospital admission for emergency patients

Enhancing self-management in type 1 diabetes with wearables and deep learning

3D Compressed Spectrum Mapping With Sampling Locations Optimization in Spectrum-Heterogeneous Environment

Deep Learning for Diabetes: A Systematic Review

Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation

Learning Vector Quantization-Aided Detection for MIMO Systems

Collaborators (7)

Sonya Crowe

Pantelis Georgiou

Zhiqiang Huo

Johan H Thygesen

Chengzhe Piao

Taiyu Zhu

Nick Oliver

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