De-accumulated error collaborative learning framework for predicting Alzheimer's disease progression

Alzheimer's disease (AD) is a chronic neurodegenerative disorder where precise prediction of progression is crucial for improving clinical diagnosis. However, missing data often complicates AD prediction in clinical practice. Existing studies primarily have employed recurrent neural networks to impute missing data, but this approach suffers from the error accumulationproblem, leading to unsatisfactory prediction results. To address this issue, this study proposes the LSTM-TSGAIN collaborative learning framework, which consists of the following three main aspects: using a generative adversarial imputation method to reduce LSTM prediction errors; collaborative training of the adversarial imputation fusion module and the time series learning module to improve the performance of the model; the model inputs were adjusted to variable lengths to accommodate the differences in the number of visits of different subjects. The effectiveness of the model is demonstrated through experiments using longitudinal data from the ADNI dataset of 1256 subjects, which shows its superiority over state-of-the-art methods.

Automated summary

This study proposes a collaborative learning framework called LSTM-TSGAIN for accurate prediction of the progression of Alzheimer's disease. The model improves prediction performance through the use of generative adversarial imputation, collaborative training, and adjusting input lengths, and experiments demonstrate its superiority over existing methods.