ArticleNeuroImage2020
A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- Thermoelectric Measuring Equipment for Perioperative Monitoring of Temperature and Heat Flux Density of the Human Eye in Vitreoretinal Surgery.Sensors (Basel, Switzerland) · 2025Article
- SOM2LM: Self-Organized Multi-Modal Longitudinal Maps.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Autism spectrum disorders detection based on multi-task transformer neural network.BMC neuroscience · 2024Article
- Seeing beyond the symptoms: biomarkers and brain regions linked to cognitive decline in Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease.Artificial intelligence in medicine · 2023Article
- Data-Driven Phenotyping of Alzheimer's Disease under Epigenetic Conditions Using Partial Volume Correction of PET Studies and Manifold Learning.Biomedicines · 2023Article
- Explainable Tensor Multi-Task Ensemble Learning Based on Brain Structure Variation for Alzheimer's Disease Dynamic Prediction.IEEE journal of translational engineering in health and medicine · 2023Article
- Whole Person Modeling: a transdisciplinary approach to mental health research.Discover mental health · 2023Review
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies.IEEE transactions on bio-medical engineering · 2022Article
- Early detection of Alzheimer's disease using neuropsychological tests: a predict-diagnose approach using neural networks.Brain informatics · 2022Article
- A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease.Sensors (Basel, Switzerland) · 2022Article
- The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.Biomedicines · 2022Review
- A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease.Frontiers in aging neuroscience · 2022Article
- PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer's Disease Feature Fusion and Multimodal Classification.Journal of Alzheimer's disease : JAD · 2021Article
- Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks.Frontiers in computational neuroscience · 2021Article
- Gaussian discriminative component analysis for early detection of Alzheimer's disease: A supervised dimensionality reduction algorithm.Journal of neuroscience methods · 2020Article
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Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algorithm for predicting the progression of Alzheimer's disease using a distributed multimodal, multitask learning method. More specifically, each individual task is defined as a regression model, which predicts cognitive scores at a single time point. Since the prediction tasks for multiple intervals are related to each other in chronological order, multitask regression models have been developed to track the relationship between subsequent tasks. Furthermore, since subjects have various combinations of recording modalities together with other genetic, neuropsychological and demographic risk factors, special attention is given to the fact that each modality may experience a specific sparsity pattern. The model is hence generalized by exploiting multiple individual multitask regression coefficient matrices for each modality. The outcome for each independent modality-specific learner is then integrated with complementary information, known as risk factor parameters, revealing the most prevalent trends of the multimodal data. This new feature space is then used as input to the gradient boosting kernel in search for a more accurate prediction. This proposed model not only captures the complex relationships between the different feature representations, but it also ignores any unrelated information which might skew the regression coefficients. Comparative assessments are made between the performance of the proposed method with several other well-established methods using different multimodal platforms. The results indicate that by capturing the interrelatedness between the different modalities and extracting only relevant information in the data, even in an incomplete longitudinal dataset, will yield minimized prediction errors.
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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 OpenQuestion graph.