Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Neurological diseases, including Alzheimer's disease (AD), are rising global health challenges. This study presents a two-stage decision support system (DSS) that uses machine learning and neuroimaging for early AD detection and monitoring. The first stage uses deep learning for predicting AD likelihood. The second leverages a 3D convolutional neural network to identify crucial brain regions in AD progression. Notably, the DSS offers a solution to machine learning's "black box" problem using an occlusion map explainability method, enhancing decision transparency. Its design is adaptable to other diseases using imaging data, underscoring its broad healthcare potential. By providing an innovative and interpretable tool for improved disease management, this research helps foster better patient care and outcomes.
Recommended Citation
Owusu, Gabriel; Wang, Xuan; and Sun, Jun, "Longitudinal healthcare analytics for early detection and progression of neurological diseases: A clinical decision support system." (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 4.
https://aisel.aisnet.org/hicss-57/hc/process/4
Longitudinal healthcare analytics for early detection and progression of neurological diseases: A clinical decision support system.
Hilton Hawaiian Village, Honolulu, Hawaii
Neurological diseases, including Alzheimer's disease (AD), are rising global health challenges. This study presents a two-stage decision support system (DSS) that uses machine learning and neuroimaging for early AD detection and monitoring. The first stage uses deep learning for predicting AD likelihood. The second leverages a 3D convolutional neural network to identify crucial brain regions in AD progression. Notably, the DSS offers a solution to machine learning's "black box" problem using an occlusion map explainability method, enhancing decision transparency. Its design is adaptable to other diseases using imaging data, underscoring its broad healthcare potential. By providing an innovative and interpretable tool for improved disease management, this research helps foster better patient care and outcomes.
https://aisel.aisnet.org/hicss-57/hc/process/4