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: | Chetanya Puri |
| Institution: | KU Leuven |
| Department: | Department of Electrical Engineering |
| Country: | |
| Proposed Analysis: | For my PhD research, I am working on devising machine learning techniques on healthcare data that can handle missing values and forecast the health condition as early as possible. One such study that we already did concerns weight gain estimation in pregnant women. In order to test the generalisation capability of our model, we would like to test it on a bigger dataset that already has been used by machine learning community (e.g. Utsumi, Y., Rudovic, O., Peterson, K., Guerrero, R., Picard, R. "Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)." The 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). May 2018.) Our technique builds a general model and tunes it according to personal data to make the forecast more personalised, resulting in reliavle predictions. (3 Chetanya Puri, Gerben Kooijman, Felipe Masculo, Shannon Van Sambeek, Sebastiaan Den Boer, Stijn Luca, and Bart Vanrumste. 2019. PREgDICT : Early Prediction of Gestational Weight Gain for Pregnancy Care. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).) We believe that our algorithm has the potential to predict key metrics of Alzheimers from ADNI database, thus helping in improving the current Alzheimer's care. We kindly request your approval for the access of this database. |
| Additional Investigators |
