ArticleScientific reports2020
Novel Alzheimer's disease subtypes identified using a data and knowledge driven strategy.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review.JMIR aging · 2025Pooled it
- Bayesian modelling demonstrates clinically relevant heterogeneity in Tau PET patterns in Alzheimer's disease.European journal of nuclear medicine and molecular imaging · 2026Article
- Radiographic phenotype-driven clustering in lumbar decompression: comparative study of outcome and reoperation risk.The spine journal : official journal of the North American Spine Society · 2026Article
- Identification and Validation of New Molecular Subtypes within the Early and Late Mild Cognitive Impairment Stages of Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer.Journal of personalized medicine · 2024Article
- Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and Biological Breakthroughs.JMIR neurotechnology · 2024Article
- Article
- Diabetic wounds and artificial intelligence: A mini-review.World journal of clinical cases · 2023Review
- The evolution of Big Data in neuroscience and neurology.Journal of big data · 2023Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment.BMC bioinformatics · 2022Article
- A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease.Brain informatics · 2022Article
- Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Review
- Recent update on the heterogeneity of the Alzheimer's disease spectrum.Journal of neural transmission (Vienna, Austria : 1996) · 2022Review
- Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?Diagnostics (Basel, Switzerland) · 2021Review
- Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations.Brain sciences · 2021Article
- Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases.International journal of molecular sciences · 2021Review
- Mitochondriopathies as a Clue to Systemic Disorders-Analytical Tools and Mitigating Measures in Context of Predictive, Preventive, and Personalized (3P) Medicine.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The population of adults with Alzheimer's disease (AD) varies in needs and outcomes. The heterogeneity of current AD diagnostic subgroups impedes the use of data analytics in clinical trial design and translation of findings into improved care. The purpose of this project was to define more clinically-homogeneous groups of AD patients and link clinical characteristics with biological markers. We used an innovative big data analysis strategy, the 3C strategy, that incorporates medical knowledge into the data analysis process. A large set of preprocessed AD Neuroimaging Initiative (ADNI) data was analyzed with 3C. The data analysis yielded 6 new disease subtypes, which differ from the assigned diagnosis types and present different patterns of clinical measures and potential biomarkers. Two of the subtypes, "Anosognosia dementia" and "Insightful dementia", differentiate between severe participants based on clinical characteristics and biomarkers. The "Uncompensated mild cognitive impairment (MCI)" subtype, demonstrates clinical, demographic and imaging differences from the "Affective MCI" subtype. Differences were also observed between the "Worried Well" and "Healthy" clusters. The use of data-driven analysis yielded sub-phenotypic clinical clusters that go beyond current diagnoses and are associated with biomarkers. Such homogenous sub-groups can potentially form the basis for enhancement of brain medicine research.
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Registered trials
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