{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/98063"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/98063","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"ADInsight: A Multimodal and Explainable Framework for Alzheimer's Disease Progression and Conversion Prediction","abstract":"ADInsight represents the crux of this dissertation, introducing an integrated and explainable framework centered on predicting Alzheimer's disease (AD) conversion, particularly for those at the early stage of mild cognitive impairment (EMCI). Beginning with an examination of models grounded in individual research modalities, such as clinical data and advanced imaging, the research underscores the potential and limitations of singular approaches. As a response to these findings, this dissertation introduces a multimodal ensemble conversion prediction model that combines Diffusion Tensor Imaging (DTI) scans with clinical data. This ensemble not only increases the accuracy of predictions but is also notable for its dedication to explainability, bridging the gap between intricate neural network predictions and understandable medical interpretations. Upon further exploration a unique framework is revealed, combining the advantages of Random Forest Regression alongside the latest over-sampling methods. This framework unravels the intricacies of AD's nonlinear progression, leading to the formulation of patient progression groupings. The dissertation is then concluded with the Cognitive Visual Recognition Tracker (CVRT) application. This application marks an exploration into cognitive focus and visual identification, which are essential elements in the development of Alzheimer's disease. Benefiting both clinicians and patients, CVRT paves the way for innovative treatment strategies. In summary, our ADInsight framework provides a novel approach to understanding and predicting the progression of AD, providing a beacon of hope and knowledge in the ongoing struggle against this debilitating condition.","abstract_html":"ADInsight represents the crux of this dissertation, introducing an integrated and explainable framework centered on predicting Alzheimer's disease (AD) conversion, particularly for those at the early stage of mild cognitive impairment (EMCI). Beginning with an examination of models grounded in individual research modalities, such as clinical data and advanced imaging, the research underscores the potential and limitations of singular approaches. As a response to these findings, this dissertation introduces a multimodal ensemble conversion prediction model that combines Diffusion Tensor Imaging (DTI) scans with clinical data. This ensemble not only increases the accuracy of predictions but is also notable for its dedication to explainability, bridging the gap between intricate neural network predictions and understandable medical interpretations. Upon further exploration a unique framework is revealed, combining the advantages of Random Forest Regression alongside the latest over-sampling methods. This framework unravels the intricacies of AD's nonlinear progression, leading to the formulation of patient progression groupings. The dissertation is then concluded with the Cognitive Visual Recognition Tracker (CVRT) application. This application marks an exploration into cognitive focus and visual identification, which are essential elements in the development of Alzheimer's disease. Benefiting both clinicians and patients, CVRT paves the way for innovative treatment strategies. 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Upon further exploration a unique framework is revealed, combining the advantages of Random Forest Regression alongside the latest over-sampling methods. This framework unravels the intricacies of AD's nonlinear progression, leading to the formulation of patient progression groupings. The dissertation is then concluded with the Cognitive Visual Recognition Tracker (CVRT) application. This application marks an exploration into cognitive focus and visual identification, which are essential elements in the development of Alzheimer's disease. Benefiting both clinicians and patients, CVRT paves the way for innovative treatment strategies. In summary, our ADInsight framework provides a novel approach to understanding and predicting the progression of AD, providing a beacon of hope and knowledge in the ongoing struggle against this debilitating condition."]},{"key":"dc:title","label":"Title","values":["ADInsight: A Multimodal and Explainable Framework for Alzheimer's Disease Progression and Conversion Prediction"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lee, Yugyung, 1960-","Gaddis, Monica Louise, 1955-"],"dc:creator":["Velazquez, Matthew J."],"dc:date.accessioned":["2024-01-30T17:05:18Z"],"dc:date.available":["2024-01-30T17:05:18Z"],"dc:date.issued":["2023"],"dc:description":["Title from PDF of title page, viewed February 1, 2024","Dissertation advisors: Yugyung Lee and Monica Gaddis","Vita","Includes bibliographical references (pages 173-186)","Dissertation (Ph.D.)--School of Computing and Engineering, Department of Biomedical and Health Informatics. 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