Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks

Solale Tabarestani; Maryamossadat Aghili; Mehdi Shojaie; Christian Freytes; Mercedes Cabrerizo; Armando Barreto; Naphtali Rishe; Rosie E. Curiel; David Loewenstein; Ranjan Duara; Malek Adjouadi

doi:10.1109/BHI.2019.8834556

Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks

Solale Tabarestani, Maryamossadat Aghili, Mehdi Shojaie, Christian Freytes, Mercedes Cabrerizo, Armando Barreto, Naphtali Rishe, Rosie E. Curiel, David Loewenstein, Ranjan Duara, Malek Adjouadi

Psychiatry & Behavioral Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728108483
DOIs	https://doi.org/10.1109/BHI.2019.8834556
State	Published - May 2019
Event	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Chicago, United States Duration: May 19 2019 → May 22 2019

Publication series

Name	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

Conference

Conference	2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
Country	United States
City	Chicago
Period	5/19/19 → 5/22/19

Keywords

Alzheimer
Classification
Diagnosis
Gated Recurrent Unit (GRU)
Long Short-Term Memory (LSTM)
Longitudinal
Multimodal
Neuroimaging
Prognosis
Regression

ASJC Scopus subject areas

Artificial Intelligence
Signal Processing
Information Systems and Management
Biomedical Engineering
Health Informatics
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/BHI.2019.8834556

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Tabarestani, S., Aghili, M., Shojaie, M., Freytes, C., Cabrerizo, M., Barreto, A., Rishe, N., Curiel, R. E., Loewenstein, D., Duara, R., & Adjouadi, M. (2019). Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings [8834556] (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2019.8834556

Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks. / Tabarestani, Solale; Aghili, Maryamossadat; Shojaie, Mehdi; Freytes, Christian; Cabrerizo, Mercedes; Barreto, Armando; Rishe, Naphtali; Curiel, Rosie E.; Loewenstein, David; Duara, Ranjan; Adjouadi, Malek.

2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8834556 (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tabarestani, S, Aghili, M, Shojaie, M, Freytes, C, Cabrerizo, M, Barreto, A, Rishe, N, Curiel, RE , Loewenstein, D, Duara, R & Adjouadi, M 2019, Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks. in 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings., 8834556, 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019, Chicago, United States, 5/19/19. https://doi.org/10.1109/BHI.2019.8834556

Tabarestani S, Aghili M, Shojaie M, Freytes C, Cabrerizo M, Barreto A et al. Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8834556. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). https://doi.org/10.1109/BHI.2019.8834556

Tabarestani, Solale ; Aghili, Maryamossadat ; Shojaie, Mehdi ; Freytes, Christian ; Cabrerizo, Mercedes ; Barreto, Armando ; Rishe, Naphtali ; Curiel, Rosie E. ; Loewenstein, David ; Duara, Ranjan ; Adjouadi, Malek. / Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks. 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings).

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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.

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