Longitudinal healthcare analytics for early detection and progression of neurological diseases: A clinical decision support system

Authors

Gabriel Owusu
Xuan Wang, The University of Texas Rio Grande Valley
Jun Sun, The University of Texas Rio Grande ValleyFollow

Document Type

Conference Proceeding

Publication Date

1-2024

Abstract

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.

Comments

Attribution-NonCommercial-NoDerivatives 4.0 International

Recommended Citation

Owusu, G., Wang, X., Sun, J., 2024. Longitudinal healthcare analytics for early detection and progression of neurological diseases: A clinical decision support system., in: Proceedings of the 57th Hawaii International Conference on System Sciences. Manoa, HI.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Publication Title

Proceedings of the 57th Hawaii International Conference on System Sciences