Cognitive Status Prediction for Alzheimer′s Disease via the Attention and LSTM-enhanced Neural Network
Yang Zelin1, Yang Kai1, Ren Xiaomei2, Xu Jun1, Zeng Xian1, Wan Zhuo1, Huang Yunzhi1#*
1(Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China) 2(College of Electrical Engineering, Sichuan University, Chengdu 610065, China)
Abstract：Alzheimer′s disease (AD) is an irreversible neurodegenerative disease, and mild cognitive impairment (MCI) is the intermediate transition state from normal control (NC) to AD, thereby MCI is a crucial monitoring state of early intervention treatment for AD. This motivates us to propose a model for predicting the cognitive status and the possible translation in the future. Our model employed the multilayer perceptron (MLP) for cognitive status recognition based on the longitudinal electronic healthy records (EHR). As the recorded data contained longitudinal changes, we integrated the attention block and long-short-term memory network (LSTM) module with the backbone MLP. In this way, our model captured both the spatial- and temporal-relationship across features at multiple timepoints. Two type experiments were conducted on 1 313 cases from ADNI dataset and 5 209 cases NACC dataset, consisting of a baseline visiting record and the following-up records every 6 months: (1) single-time-point cognitive status prediction with multiple previous visiting records, and (2) multiple-time-point cognitive statuses simultaneously prediction with merely previous single-time-point visiting record. The proposed method made a desirable prediction on the cognitive status. Especially, based on the trained models over the 2nd, 3rd, 5th, and 6th time point records, we achieved average F2 scores of 0.87, 0.88, 0.91, and 0.91 when predicting the corresponding subsequential single-time-point cognitive status, respectively; and achieved average F2 scores of 0.84, 0.86, 0.88, and 0.89 when predicting the corresponding next two-, three-, and four-time points cognitive status, respectively. Compared to the gated recurrent unit, the corresponding F2 scores were improved by 0.03, 0.01, 0.03, and 0.02 when predicting the corresponding subsequential single-time-point cognitive status, respectively, and the average F2 scores were improved by 0.04, 0.05, 0.02, and 0.02 when predicting the corresponding next two-, three-, and four-time points cognitive status, respectively. These results demonstrated a superior performance of the proposed method in predicting cognitive status as compared to the state-of-the-art methods, e.g., MLP, gated recurrent unit and the traditional machine learning classifiers, and can be useful for early intervention treatment for AD patients.
杨泽霖, 杨凯, 任小梅, 徐军, 曾娴, 万卓, 黄韫栀. 基于注意力机制和长短时记忆网络增强的阿尔茨海默症早期预测[J]. 中国生物医学工程学报, 2025, 44(6): 641-653.
Yang Zelin, Yang Kai, Ren Xiaomei, Xu Jun, Zeng Xian, Wan Zhuo, Huang Yunzhi. Cognitive Status Prediction for Alzheimer′s Disease via the Attention and LSTM-enhanced Neural Network. Chinese Journal of Biomedical Engineering, 2025, 44(6): 641-653.
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