ArticleNeurobiology of aging2023
Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis.
Article in Neurobiology of aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
27 citing papers in PubMed, 39 citations in OpenAlex.
- National trends and disparities in Alzheimer disease's mortality (1999-2023) with scenario projections and a data-driven LSTM sensitivity analysis to 2043.European archives of psychiatry and clinical neuroscience · 2026Article
- APOE ε4 Allele Dose and Time to Clinical Conversion from Mild Cognitive Impairment to Alzheimer's Disease Dementia: An ADNI Survival Analysis.Biomedicines · 2026Article
- Deep survival modeling to predict future cognitive impairment in unimpaired adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study.Scientific reports · 2026Article
- Longitudinal multi-modal data prediction model for mild cognitive impairment by deep survival analysis.BMC medical informatics and decision making · 2026Article
- Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.Brain informatics · 2026Review
- Article
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- Predictive factors for dementia among older adults in South Korea: an interpretable machine learning analysis.Alzheimer's research & therapy · 2025Article
- Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.BMC medical research methodology · 2025Article
- Multi-Modal Deep Clustering Survival Machines for Alzheimer's Disease Subtype Discovery.... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision · 2025Article
- Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study.Research square · 2025Article
- Alzheimer's disease risk prediction using machine learning for survival analysis with a comorbidity-based approach.Scientific reports · 2025Article
- Longitudinal Alzheimer's Disease Progression Prediction With Modality Uncertainty and Optimization of Information Flow.IEEE journal of biomedical and health informatics · 2025Article
- HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.JAMIA open · 2024Article
- Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling.NPJ digital medicine · 2024Article
- Article
- Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology.Journal of clinical medicine · 2024Review
- Early detection of dementia with default-mode network effective connectivity.Nature. Mental health · 2024Article
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 3 countries.
Funding
Abstract
Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects' time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features.
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Registered trials
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 Socratic graph.