Early Detection of Alzheimer's Disease
This project aims to develop models that analyze short speeches encompassing various topics, such as daily activities, room environments, and animated pictures. The objective is to determine whether the speaker exhibits signs of Mild Cognitive Impairment (MCI), which is recognized as an early stage of Alzheimer's Disease.
- PI: Ihab Hajjar - Associate Professor of Medicine, Emory University
- Co-I: Jinho Choi - Assistant Professor of Computer Science, Emory University
- Development of Digital Voice Biomarkers and Associations with Cognition, CSF Biomarkers and Neural Representation in Early Alzheimer’s Disease. Hajjar, I.; Choi, J. D.; Moore, E.; Okafor, M.; Abrol, A.; Calhoun, V. D.; Goldstein, F. C. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM), 2023.
- Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP Dataset for Early Detection of Alzheimer's Disease. Li, R. A.; Hajjar; Ihab; Goldstein, F.; and Choi, J. D. Proceedings of the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL), 2020.
- Meta-Semantic Representation for Early Detection of Alzheimer's Disease. Choi, J. D.; Li, M.; Goldstein, F.; and Hajjar, I. Proceedings of the ACL Workshop on Designing Meaning Representations (DMR), 2019.