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: | Alizée Pace |
| Institution: | University of Cambridge |
| Department: | Department of Engineering |
| Country: | |
| Proposed Analysis: | The goal of this analysis is to build algorithms to better understand and explain the process of clinical decision-making, with the hope of improving clinical decision support, uncovering variation in practice, and building robust guidelines for care. Rather than replacing physicians with autonomous agents trained to optimize patient outcomes, this work aims to better describe and understand existing treatment policies, in a form of quantitative epistemology. In practice, the proposed method should capture how and why physicians assign treatments given a history of patient observations, using an interpretable model for policy learning. The ADNI dataset will be used to demonstrate and validate the effectiveness of this approach, and the results of this work will be published at a peer-reviewed Machine Learning conference or medical journal. |
| Additional Investigators |
