| Title: | Application of machine learning for classification of late-life diseases and their outcome prediction |
| Lead Institution: | Shenzhen Kangning Hospital |
| Principal investigator: | Dr Xi-jian Dai |
Application 75732
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About
First, we use the machine learning algorithms to differentiate patients with late-life diseases from those without late-life diseases for early diagnosis, and tell us what and where our brain changed. Second, we used the magnetic Resonance Imaging (MRI) data at baseline to predict their outcome endpoints of late-life diseases in the longitudinal data at follow-up, and tell us what and where our brain changed when one disease progress to another disease. Third, we used the MRI data at baseline to predict their brain age, and tell us whether and why our brain are elder when we have a diagnosis of late-life diseases. Fourth, we will use the baseline data to build a risk prediction and evaluation system. This system will tell you the risk of developing a late-life disease when you have poor lifestyle or other adverse events. These finding may broaden and expand our understanding of the neurobiological mechanism of late-life diseases. In view of huge works on data cleansing and data analyses, this project will last for 36 months.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 8162 | Association Between Sleep Duration and Parkinson's Disease Varied Across Related Orphan Receptor A rs2028122 Genotypes | Yuan Shao (+3) | 2022 | Frontiers in Neuroscience |
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