TY - GEN
T1 - Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks
AU - Tabarestani, Solale
AU - Aghili, Maryamossadat
AU - Shojaie, Mehdi
AU - Freytes, Christian
AU - Cabrerizo, Mercedes
AU - Barreto, Armando
AU - Rishe, Naphtali
AU - Curiel, Rosie E.
AU - Loewenstein, David
AU - Duara, Ranjan
AU - Adjouadi, Malek
N1 - Funding Information:
This research has been supported by the National Science Foundation (NSF) under NSF grants CNS-1532061, CNS-1429345, CNS-1551221, and CNS-1338922. We also greatly appreciate the support of the 1Florida Alzheimer’s Disease Research Center (ADRC) (NIA 1P50AG047266-01A1) and the Ware Foundation. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from other sources. REFERENCES [1] R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, MO.
PY - 2019/5
Y1 - 2019/5
N2 - This paper proposes an implementation of Recurrent Neural Networks (RNNs) for (a) predicting future Mini-Mental State Examination (MMSE) scores in a longitudinal study and (b) deploying a multiclass multimodal neuroimaging classification process that involves three different known stages of Alzheimer's progression, cognitively normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). This multimodal data is fed into two well-studied variations of the RNNs; Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The accuracy, F-score, sensitivity, and specificity of the models are reported for the classification task as well as the root mean square error (RMSE) and correlation coefficient for the regression task. The results demonstrate the superiority of the proposed model over state-of-The-Art classification and regression techniques of Support Vector Machine (SVM), Support Vector Regression (SVR) and Ridge Regression.
AB - This paper proposes an implementation of Recurrent Neural Networks (RNNs) for (a) predicting future Mini-Mental State Examination (MMSE) scores in a longitudinal study and (b) deploying a multiclass multimodal neuroimaging classification process that involves three different known stages of Alzheimer's progression, cognitively normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). This multimodal data is fed into two well-studied variations of the RNNs; Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The accuracy, F-score, sensitivity, and specificity of the models are reported for the classification task as well as the root mean square error (RMSE) and correlation coefficient for the regression task. The results demonstrate the superiority of the proposed model over state-of-The-Art classification and regression techniques of Support Vector Machine (SVM), Support Vector Regression (SVR) and Ridge Regression.
KW - Alzheimer
KW - Classification
KW - Diagnosis
KW - Gated Recurrent Unit (GRU)
KW - Long Short-Term Memory (LSTM)
KW - Longitudinal
KW - Multimodal
KW - Neuroimaging
KW - Prognosis
KW - Regression
UR - http://www.scopus.com/inward/record.url?scp=85073008151&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073008151&partnerID=8YFLogxK
U2 - 10.1109/BHI.2019.8834556
DO - 10.1109/BHI.2019.8834556
M3 - Conference contribution
AN - SCOPUS:85073008151
T3 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
BT - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
Y2 - 19 May 2019 through 22 May 2019
ER -