TA‐RNN: an Attention‐based Time‐aware Recurrent Neural Network Architecture to Predict Progression of Alzheimer’s Disease

We propose TA-RNN, an interpretable model to predict MCI to AD conversion while handling irregular time intervals. TA-RNN outperformed SOTA and baseline methods in multiple experiments.

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Publication Details

Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.089010
Citations
1
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.35
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article

TA‐RNN: an Attention‐based Time‐aware Recurrent Neural Network Architecture to Predict Progression of Alzheimer’s Disease

Mohammad Al Olaimat, Serdar Bozdag, Fahad Saeed
1 citations
Alzheimer s & Dementia
Machine Learning in Healthcare
0.35
article

TA‐RNN: an Attention‐based Time‐aware Recurrent Neural Network Architecture to Predict Progression of Alzheimer’s Disease

Mohammad Al Olaimat, Serdar Bozdag, Fahad Saeed
article en
1 citations

Abstract

We propose TA-RNN, an interpretable model to predict MCI to AD conversion while handling irregular time intervals. TA-RNN outperformed SOTA and baseline methods in multiple experiments.

Alzheimer s & DementiaVol. 20(S1)
University of North Texas (US), Florida International University (US)
Openalex Percentile: Top 30%
Machine Learning in Healthcare
0.35
DOIOpen Access PDFOpenAlex
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