ArticleScientific reports2018
A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data.
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 20 papers.
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Who cites it
20 citing papers in PubMed.
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- Evaluating Homoscedastic Uncertainty Weighting and Generative Tau Imputation in a Multimodal, Multitask Deep Learning Framework for Alzheimer's Disease.Neuroinformatics · 2026Article
- A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers.Frontiers in aging neuroscience · 2025Review
- Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification.Healthcare technology letters · 2024Article
- HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.JAMIA open · 2024Article
- Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures.Sensors (Basel, Switzerland) · 2022Article
- Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review.Computational and structural biotechnology journal · 2022Review
- Alzheimer's Disease Assessments Optimized for Diagnostic Accuracy and Administration Time.IEEE journal of translational engineering in health and medicine · 2022Article
- Improved Alzheimer's Disease Detection by MRI Using Multimodal Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2021Article
- Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers.Sensors (Basel, Switzerland) · 2021Article
- A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining.Diagnostics (Basel, Switzerland) · 2021Review
- Association of the use of hearing aids with the conversion from mild cognitive impairment to dementia and progression of dementia: A longitudinal retrospective study.Alzheimer's & dementia (New York, N. Y.) · 2021Article
- Shaping a data-driven era in dementia care pathway through computational neurology approaches.BMC medicine · 2020Review
- Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study.JMIR medical informatics · 2020Article
- Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease.Neuropharmacology · 2020Review
- Multi-time-point data preparation robustly reveals MCI and dementia risk factors.Alzheimer's & dementia (Amsterdam, Netherlands) · 2020Article
- When Does Alzheimer's Disease Really Start? The Role of Biomarkers.International journal of molecular sciences · 2019Review
- A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual.Expert systems with applications · 2019Article
- Challenges of Integrative Disease Modeling in Alzheimer's Disease.Frontiers in molecular biosciences · 2019Review
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12 authors.
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Abstract
There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.
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