| Title: | Application of machine learning and risk prediction model in late-life diseases |
| Lead Institution: | Nanchang University |
| Principal investigator: |
Application 94653
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About
This project will find what and where our brain changed when we have a diagnosis of late-life disease. These finding may broaden and expand our understanding of the neurobiological mechanism of late-life diseases. Furthermore, we will find risk and protective factors for late-life diseases. Next, we will develop a risk prediction system based on these factors to tell us what is the risk of developing this disease in the future. This system will could help us to identify our potential risk profile and provide guidance on precise and timely actions to prevent or delay to develop late-life disease. In view of huge works on data cleansing and data analyses, this project will last for 36 months.4 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 8269 | Activation network improves spatiotemporal modelling of human brain communication processes | Xucheng Liu (+8) | 2023 | NeuroImage |
| 20913 | Associations of phenotypic aging with dementia-free life expectancy among 45,608 participants with prediabetes: a prospective cohort study | Zheng Zhang (+3) | 2026 | Acta Diabetologica |
| 15855 | Development and validation of a convenient dementia risk prediction tool for diabetic population: A large and longitudinal machine learning cohort study | Pei Yang (+8) | 2025 | Journal of Affective Disorders |
| 15378 | Development and validation of a novel predictive model for dementia risk in middle-aged and elderly depression individuals: a large and longitudinal machine learning cohort study | Xuan Xiao (+8) | 2025 | Alzheimer's Research & Therapy |
Enabling scientific discoveries that improve human health