ACDNet: Clinical prediction with electronic health records
First-author work on clinical prediction from patient electronic health records. Add the full citation, DOI, and PDF link here.
University of Glasgow
Jiacong Mi
Ph.D. student in Public Health (AI for Science), working on biomedical AI, electronic health records, graph learning, and clinical decision support.
About
Research at the intersection of patients, drugs, and data.
I am a Ph.D. student at the University of Glasgow. My work focuses on machine learning methods for biomedical data analysis, with an emphasis on longitudinal electronic health records, clinical prediction, interpretable models, and AI-driven drug discovery.
Before Glasgow, I worked as a research assistant at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. I received master's degrees from Monash University and Southeast University, and a bachelor's degree in Computer Science and Technology from Shandong Normal University.
Research
Current interests
Biomedical AI
Foundation Models for Science
Graph Neural Networks
Self-supervised Learning
Multimodal Learning
Interpretable AI
EHR Mining
AI-driven Drug Discovery
Clinical Decision Support
Chronic Disease Management
Publications
Selected work
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Experience
Academic path
Ph.D. in Public Health (AI for Science)
University of Glasgow. Supervisor: Prof. Honghan Wu.
Research Assistant
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences.
Master's Degrees
Monash University and Southeast University, with research on medication recommendation and clinical prediction from EHR data.
Bachelor of Computer Science and Technology
Shandong Normal University.
Projects
Selected projects
TCM Knowledge Graph Tool
A Traditional Chinese Medicine knowledge graph system with data management operations backed by Neo4j and Flask.
AI Drug Discovery Models
Molecular generation and toxicity prediction work from internships at neoX Biotech and Insilico Medicine.
Contact