Observational studyAlzheimer's & dementia : the journal of the Alzheimer's Association2018
A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data.
Observational study in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- Systems biology of Alzheimer's Disease: a scoping review of key pathways and mechanisms.Molecular neurodegeneration · 2026Article
- Normalizing flow based neural processes for Alzheimer's disease progression prediction.PloS one · 2026Article
- Ensemble survival analysis for preclinical cognitive decline prediction in Alzheimer's disease using longitudinal biomarkers.Journal of Alzheimer's disease : JAD · 2025Article
- Dynamic Prognostic Models for Colorectal Cancer With Liver Metastases.JAMA network open · 2025Article
- A dynamic prediction model for predicting the time at which patients with MCI progress to AD based on time-dependent covariates.BMC medical informatics and decision making · 2025Article
- Characterizing bidirectional transitions in mild cognitive impairment and post-reversion based on longitudinal neuroimaging and cognitive assessments.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.Clinical pharmacology and therapeutics · 2025Review
- Early prediction of Alzheimer's disease using artificial intelligence and cortical features on T1WI sequences.Frontiers in neurology · 2025Article
- Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment.Frontiers in aging neuroscience · 2024Article
- Neuroimaging techniques, gene therapy, and gut microbiota: frontier advances and integrated applications in Alzheimer's Disease research.Frontiers in aging neuroscience · 2024Review
- Predicting Alzheimer's Disease and Quantifying Asymmetric Degeneration of the Hippocampus Using Deep Learning of Magnetic Resonance Imaging Data.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Constructing a prognostic risk model for Alzheimer's disease based on ferroptosis.Frontiers in aging neuroscience · 2023Article
- A classification for complex imbalanced data in disease screening and early diagnosis.Statistics in medicine · 2022Article
- On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer's Disease Using Neuroimaging Modalities and Data Augmentation Methods.Journal of healthcare engineering · 2022Article
- Validation and Normative Data of the Spanish Version of the Rey Auditory Verbal Learning Test and Associated Long-Term Forgetting Measures in Middle-Aged Adults.Frontiers in aging neuroscience · 2022Article
- Prognostic Modeling of Parkinson's Disease Progression Using Early Longitudinal Patterns of Change.Movement disorders : official journal of the Movement Disorder Society · 2021Article
- Article
- A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
introductionCharacterizing progression in Alzheimer's disease is critically important for early detection and targeted treatment. The objective was to develop a prognostic model, based on multivariate longitudinal markers, for predicting progression-free survival in patients with mild cognitive impairment.
methodsThe information contained in multiple longitudinal markers was extracted using multivariate functional principal components analysis and used as predictors in the Cox regression models. Cross-validation was used for selecting the best model based on Alzheimer's Disease Neuroimaging Initiative-1. External validation was conducted on Alzheimer's Disease Neuroimaging Initiative-2.
resultsModel comparison yielded a prognostic index computed as the weighted combination of historical information of five neurocognitive longitudinal markers that are routinely collected in observational studies. The comprehensive validity analysis provided solid evidence of the usefulness of the model for predicting Alzheimer's disease progression. DISCUSSION: The prognostic model was improved by incorporating multiple longitudinal markers. It is useful for monitoring disease and identifying patients for clinical trial recruitment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.