计算机科学
推论
编码器
深度学习
人工智能
序列(生物学)
接收机工作特性
疾病
机器学习
模式识别(心理学)
语音识别
医学
遗传学
生物
操作系统
病理
作者
Km Poonam,Rajlakshmi Guha,P. P. Chakrabarti
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-10
标识
DOI:10.1109/jbhi.2024.3386801
摘要
Detecting Alzheimer's disease (AD) accurately at an early stage is critical for planning and implementing disease-modifying treatments that can help prevent the progression to severe stages of the disease. In the existing literature, diagnostic test scores and clinical status have been provided for specific time points, and predicting the disease progression poses a significant challenge. However, few studies focus on longitudinal data to build deep-learning models for AD detection. These models are not stable to be relied upon in real medical settings due to a lack of adaptive training and testing. We aim to predict the individual's diagnostic status for the next six years in an adaptive manner where prediction performance improves with the number of patient visits. This study presents a Sequence-Length Adaptive Encoder-Decoder Long Short-Term Memory (SLA-ED LSTM) deep-learning model on longitudinal data obtained from the Alzheimer's Disease Neuroimaging Initiative archive. In the suggested approach, decoder LSTM dynamically adjusts to accommodate variations in training sequence length and inference length rather than being constrained to a fixed length. We evaluated the model performance for various sequence lengths and found that for inference length one, sequence length nine gives the highest average test accuracy and area under the receiver operating characteristic curves of 0.920 and 0.982, respectively. This insight suggests that data from nine visits effectively captures meaningful cognitive status changes and is adequate for accurate model training. We conducted a comparative analysis of the proposed model against state-of-the-art methods, revealing a significant improvement in disease progression prediction over the previous methods. Index Terms- Cognitive impairment, longitudinal data, multimodal data, encoder-decoder LSTM, progression prediction Clinical relevance The proposed approach has the potential to improve understanding of Alzheimer's disease progression in diagnostics, facilitating early identification of various stages of cognitive decline leading to AD by considering its clinical variability.
科研通智能强力驱动
Strongly Powered by AbleSci AI