ArticleScientific reports2018
Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Comparative Analysis of Deep Learning Techniques in Alzheimer's Disease Diagnosis: Trends, Challenges, and Future Directions.MethodsX · 2026Review
- A dynamic risk prediction framework for Alzheimer's disease and related dementias with interpretability.NPJ digital medicine · 2026Article
- Evaluating Homoscedastic Uncertainty Weighting and Generative Tau Imputation in a Multimodal, Multitask Deep Learning Framework for Alzheimer's Disease.Neuroinformatics · 2026Article
- Role of Pentacyclic Triterpenes in the Management of Neurological Disorders: An Insight into Molecular Mechanisms and Therapeutic Approaches.Molecular neurobiology · 2026Review
- Staging and Pseudotime Inference of Alzheimer's Disease Progression using Multimodal Imaging Data.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- A Self-Explainable Dynamic Risk Monitoring Framework for Predicting Alzheimer's Disease and Related Dementias.medRxiv : the preprint server for health sciences · 2025Article
- A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis.Current topics in medicinal chemistry · 2025Review
- Multi-Class Detection of Neurodegenerative Diseases from EEG Signals Using Lightweight LSTM Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Building and validating trend-based multiple sclerosis case definitions: a population-based cohort study for Manitoba, Canada.BMJ open · 2024Article
- MAPPING ALZHEIMER'S DISEASE PSEUDO-PROGRESSION WITH MULTIMODAL BIOMARKER TRAJECTORY EMBEDDINGS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Visual acuity prediction on real-life patient data using a machine learning based multistage system.Scientific reports · 2024Article
- Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia.Journal of neurology · 2024Article
- Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.medRxiv : the preprint server for health sciences · 2024Article
- An explainable machine learning approach for Alzheimer's disease classification.Scientific reports · 2024Article
- Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis.Health care science · 2024Article
- Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-effective Model Selection Algorithm.IEEE access : practical innovations, open solutions · 2024Article
- Prediction of chronic kidney disease progression using recurrent neural network and electronic health records.Scientific reports · 2023Article
- Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
The number of service visits of Alzheimer's disease (AD) patients is different from each other and their visit time intervals are non-uniform. Although the literature has revealed many approaches in disease progression modeling, they fail to leverage these time-relevant part of patients' medical records in predicting disease's future status. This paper investigates how to predict the AD progression for a patient's next medical visit through leveraging heterogeneous medical data. Data provided by the National Alzheimer's Coordinating Center includes 5432 patients with probable AD from August 31, 2005 to May 25, 2017. Long short-term memory recurrent neural networks (RNN) are adopted. The approach relies on an enhanced "many-to-one" RNN architecture to support the shift of time steps. Hence, the approach can deal with patients' various numbers of visits and uneven time intervals. The results show that the proposed approach can be utilized to predict patients' AD progressions on their next visits with over 99% accuracy, significantly outperforming classic baseline methods. This study confirms that RNN can effectively solve the AD progression prediction problem by fully leveraging the inherent temporal and medical patterns derived from patients' historical visits. More promisingly, the approach can be customarily applied to other chronic disease progression problems.
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