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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728108483
DOIs
StatePublished - May 2019
Event2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Chicago, United States
Duration: May 19 2019May 22 2019

Publication series

Name2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

Conference

Conference2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
CountryUnited States
CityChicago
Period5/19/195/22/19

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

Fingerprint Dive into the research topics of 'Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks'. Together they form a unique fingerprint.

  • Cite this

    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