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: | May Yong |
| Institution: | Alan Turing Institute |
| Department: | Research Engineering Group |
| Country: | |
| Proposed Analysis: | Our collaborators are Cambridge are deploying an algorithm to stratify patients with Alzheimer's from healthy patients, in a memory clinic. Our team of research software engineers are building the machine learning support infrastructure to silently monitor the performance of the algorithm on new patients in a real life setting. We are applying for access to ADNI so that we can build baseline metrics of ADNI data so that we can compare the distribution of new patients against ADNI participants. The purpose of this analysis is to detect when the algorithm is not performing optimally in real life, compared to its performance on existing datasets. |
| Additional Investigators | |
| Investigator's Name: | Oscar Giles |
| Proposed Analysis: | Our collaborators are Cambridge are deploying an algorithm to stratify patients with Alzheimer's from healthy patients, in a memory clinic. Our team of research software engineers are building the machine learning support infrastructure to silently monitor the performance of the algorithm on new patients in a real life setting. We are applying for access to ADNI so that we can build baseline metrics of ADNI data so that we can compare the distribution of new patients against ADNI participants. The purpose of this analysis is to detect when the algorithm is not performing optimally in real life, compared to its performance on existing datasets. |
| Investigator's Name: | Mahed Abroshan |
| Proposed Analysis: | Our collaborators are Cambridge are deploying an algorithm to stratify patients with Alzheimer's from healthy patients, in a memory clinic. Our team of research software engineers are building the machine learning support infrastructure to silently monitor the performance of the algorithm on new patients in a real life setting. We are applying for access to ADNI so that we can build baseline metrics of ADNI data so that we can compare the distribution of new patients against ADNI participants. The purpose of this analysis is to detect when the algorithm is not performing optimally in real life, compared to its performance on existing datasets. |
| Investigator's Name: | Jannetta Steyn |
| Proposed Analysis: | Our collaborators are Cambridge are deploying an algorithm to stratify patients with Alzheimer's from healthy patients, in a memory clinic. Our team of research software engineers are building the machine learning support infrastructure to silently monitor the performance of the algorithm on new patients in a real life setting. We are applying for access to ADNI so that we can build baseline metrics of ADNI data so that we can compare the distribution of new patients against ADNI participants. The purpose of this analysis is to detect when the algorithm is not performing optimally in real life, compared to its performance on existing datasets. |
