There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Clarissa Sun |
| Institution: | The Chinese University of HongKong, Shenzhen |
| Department: | School of science and engineering |
| Country: | |
| Proposed Analysis: | The aim of the study is to answer the following research questions: · How predictable is progression to Alzheimer’s Disease (AD) in at-risk individuals? · Which data, processing pipelines, and predictive models best predict future AD progression? · Can we use such methods to improve cohort selection for clinical trials? We will answer these questions by developing an explainable predictive machine learning model from the 4 "standard" data sets provided by TADPOLE. · D1 - a comprehensive longitudinal data set for training; · D2 - a comprehensive longitudinal data set on rollover subjects for forecasting; · D3 - a limited forecasting data set on the same rollover subjects as D2. D4 - the future data set for testing We will try different types of predictive machine learning algorithms including xgboost, random forest, dnn and integrate a explainable machine learning approach—Shapley value, to evaluate different driven forces for the progression of alzheimer’s disease. |
| Additional Investigators |
