Early Detection of Alzheimer's Disease
This project develops models to analyze short speeches describing various topics (e.g., daily activities, room environments, animated pictures) and determine whether or not the speaker has Mild Cognitive Impairment (MCI) that is considered to be an early stage of Alzheimer's Disease.
- PI: Ihab Hajjar - Associate Professor of Medicine, Emory University
- Co-I: Felicia Goldstein - Professor of Neurology, 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.
Prediction of Hospital Readmission for Kidney Transplant
This project develops models to jointly analyze diverse types of clinical notes (e.g., consultations, discharge summary, echocardiography, history and physical, operative, progress, selection conference, social worker) and predict whether or not a patient would be re-hospitalized.
- PI: Rachel Patzer - Associate Professor of Surgery, Emory University
- NLP: Jinho Choi - Assistant Professor of Computer Science, Emory University
- Predicting Kidney Transplant Recipient Cohorts’ 30-Day Re- Hospitalization Using Clinical Notes and Electronic Healthcare Record Data. Arenson, M.; Hogan, J.; Xu, L.; Lynch, R.; Lee, Y. H.; Choi, J. D.; Sun, J.; Adams, A.; Patzer, R. Kidney International Reports (KIR), 2023.
- Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning. Xu, L.; Hogan, J.; Patzer, R. E.; and Choi, J. D. Proceedings of the ACL Workshop on Biomedical Natural Language Processing (BioNLP), 2020.
- Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission. Shin, B.; Hogan, J.; Adams, A. B.; Lynch, R. J.; Patzer, R. E.; and Choi, J. D. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2019.
Detection of Neurologic Symptoms in Head CT
This project develops models to analyze radiology reports on Head Computed Tomography (CT) and detec neurology symptoms such as Severity of Study, Acute Blood, Mass Effect, Acute Stroke, and Hydrocephalus.
- PI: Falgun Chokshi - Assistant Professor of Radiology, Emory University
- Co-PI: Jinho Choi - Assistant Professor of Computer Science, Emory University
- Natural Language Processing for Classification of Acute, Communicable Findings on Unstructured Head CT Reports: Comparison of Neural Network and Non-Neural Machine Learning Techniques. Chokshi, F.; Shin, B.; Lee, T.; Lemmon, A.; Necessary, S.; and Choi, J. D. bioRxiv, 173310, 2017.
- Classification of Radiology Reports Using Neural Attention Models. Shin, B.; Chokshi, F. H.; Lee, T.; and Choi, J. D. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2017.