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Principal Investigator  
Principal Investigator's Name: Madalina Fiterau
Institution: University of Massachusetts Amherst
Department: Computer Science
Country:
Proposed Analysis: This project will develop methods for the early detection of Alzheimer’s disease from longitudinally collected data, including brain MRIs. We will introduce new techniques to extract representations from medical images and incorporate them in an end-to-end predictive framework. Such methods will be useful for modeling, monitoring and forecasting the progression of Alzheimer's disease and other chronic conditions, where MRIs or X-ray images accompany the clinical information collected at different levels of granularity. First, we will model the trajectories of chronic conditions and forecast long-term clinical outcomes. We leverage multi-resolution longitudinal data such as electronic health records that contain MRI and other unstructured information. Second, we learn features from MRIs, integrated with the rest of the model, to improve performance over using only standard features. Third, we identify individuals at risk for developing Alzheimer’s based on our extraction of information from existing patient databases. At the end of this study, we will have created a general forecasting framework capable of predicting the onset of Alzheimer’s years before symptoms arise, a striking advance that will enable clinicians to identify new prevention strategies and prepare for, rather that respond to, Alzheimer’s.
Additional Investigators  
Investigator's Name: Yeahuay Wu
Proposed Analysis: This project will develop methods for the early detection of Alzheimer’s disease from longitudinally collected data, including brain MRIs. We will introduce new techniques to extract representations from medical images and incorporate them in an end-to-end predictive framework. Such methods will be useful for modeling, monitoring and forecasting the progression of Alzheimer's disease and other chronic conditions, where MRIs or X-ray images accompany the clinical information collected at different levels of granularity. First, we will model the trajectories of chronic conditions and forecast long-term clinical outcomes. We leverage multi-resolution longitudinal data such as electronic health records that contain MRI and other unstructured information. Second, we learn features from MRIs, integrated with the rest of the model, to improve performance over using only standard features. Third, we identify individuals at risk for developing Alzheimer’s based on our extraction of information from existing patient databases. At the end of this study, we will have created a general forecasting framework capable of predicting the onset of Alzheimer’s years before symptoms arise, a striking advance that will enable clinicians to identify new prevention strategies and prepare for, rather that respond to, Alzheimer’s.
Investigator's Name: Yi Fung
Proposed Analysis: This project will develop methods for the early detection of Alzheimer’s disease from longitudinally collected data, including brain MRIs. We will introduce new techniques to extract representations from medical images and incorporate them in an end-to-end predictive framework. Such methods will be useful for modeling, monitoring and forecasting the progression of Alzheimer's disease and other chronic conditions, where MRIs or X-ray images accompany the clinical information collected at different levels of granularity. First, we will model the trajectories of chronic conditions and forecast long-term clinical outcomes. We leverage multi-resolution longitudinal data such as electronic health records that contain MRI and other unstructured information. Second, we learn features from MRIs, integrated with the rest of the model, to improve performance over using only standard features. Third, we identify individuals at risk for developing Alzheimer’s based on our extraction of information from existing patient databases. At the end of this study, we will have created a general forecasting framework capable of predicting the onset of Alzheimer’s years before symptoms arise, a striking advance that will enable clinicians to identify new prevention strategies and prepare for, rather that respond to, Alzheimer’s.
Investigator's Name: Sidong Zhang
Proposed Analysis: This project will develop methods for the early detection of Alzheimer’s disease from longitudinally collected data, including brain MRIs. We will introduce new techniques to extract representations from medical images and incorporate them in an end-to-end predictive framework. Such methods will be useful for modeling, monitoring and forecasting the progression of Alzheimer's disease and other chronic conditions, where MRIs or X-ray images accompany the clinical information collected at different levels of granularity. First, we will model the trajectories of chronic conditions and forecast long-term clinical outcomes. We leverage multi-resolution longitudinal data such as electronic health records that contain MRI and other unstructured information. Second, we learn features from MRIs, integrated with the rest of the model, to improve performance over using only standard features. Third, we identify individuals at risk for developing Alzheimer’s based on our extraction of information from existing patient databases. At the end of this study, we will have created a general forecasting framework capable of predicting the onset of Alzheimer’s years before symptoms arise, a striking advance that will enable clinicians to identify new prevention strategies and prepare for, rather that respond to, Alzheimer’s.
Investigator's Name: Genglin Liu
Proposed Analysis: Genglin's research focuses on forecasting AD based on the tadpole features as well as the MRI images. Specifically, he will be using a deep learning model to extract additional features from the MRIs that are available in the dataset and using them in the forecasting pipeline which uses an RNN model.
Investigator's Name: James Ko
Proposed Analysis: James will be assisting with the research on forecasting AD based on the tadpole features as well as the MRI images. Specifically, he will be using a deep learning model to extract additional features from the MRIs that are available in the dataset and using them in the forecasting pipeline which uses an RNN model.