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: | Robin Mae Schreiner |
| Institution: | University of Sussex |
| Department: | Informatics |
| Country: | |
| Proposed Analysis: | The ADNI data will be used to accurately, and with a degree of confidence, predict the Alzheimer’s disease (AD) onset in patients. The TADPOLE Grand Challenge carried out in 2017, provides three pre-defined comprehensive longitudinal sub-datasets for training, validation, and testing of predictive models which will be used. Systematic underrepresentation of minorities in this data can result in misclassification and biased predictions. Especially in the health care sector, this can lead to fatal consequences when action taking is being delayed or incorrect treatment plans are being proposed as a result of a patient’s predicted AD onset. Therefore, a predictive machine learning model will be built to accurately take into consideration subject variability to determine the most informative subject groups in the data. These specific patient groups should be included in future clinical study cohorts to complete training datasets. The completed data sets will be more balanced and less biased toward minorities, thus allowing us to build predictive models that treat patients fairly. |
| Additional Investigators |
