| Title: | Research on chronic disease progression |
| Lead Institution: | Zhejiang University |
| Principal investigator: |
Application 89757
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About
(1)Aims: Our research will utilize artificial intelligence techniques for identifying the latent chronic disease progression patterns from the multidimensional and longitudinal chronic disease data, and provide accurate analysis techniques for the evolution of chronic diseases. (2)Scientific rationale: Electronic health records document is consistent with the actual clinical trajectories of chronic disease patients, and thus provide huge potential to be explored for chronic diseases progression analysis. Using electronic medical records combined with artificial intelligence technology to mine the chronic disease progression, can reveal the regularity of the occurrence, development and evolution of chronic diseases, assist clinicians to intervene accurately and improve the results of diagnosis and treatment. (3)Project duration: The period of this study is 3 years. (4)Public health impact: The methodologies and experiences learned from the proposed project, would not only pave the way for the efficient management, analysis and decision-making of chronic diseases, but also, from a more general perspective, contribute to meaningful or secondary use of big clinical data for health service management and clinical decision support.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 15620 | Genetically supported targets and drug repurposing for brain aging: A systematic study in the UK Biobank | Fan Yi (+7) | 2025 | Science Advances |
| 15192 | Machine learning reveals connections between preclinical type 2 diabetes subtypes and brain health | Fan Yi (+8) | 2025 | Brain |
Enabling scientific discoveries that improve human health