ArticlePloS one2013
Alzheimer's disease risk assessment using large-scale machine learning methods.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment.The Cochrane database of systematic reviews · 2020Pooled it
- Advancing Alzheimer's research: A review of big data promises.International journal of medical informatics · 2017Pooled it
- Article
- Association of epigenetic age acceleration with MRI biomarkers of aging and Alzheimer's disease neurodegeneration.medRxiv : the preprint server for health sciences · 2026Article
- Comparison of Machine Learning Algorithms Identifying Children at Increased Risk of Out-of-Home Placement: Development and Practical Considerations.Health services research · 2025Article
- Machine Learning Approaches for Predicting Progression to Alzheimer's Disease in Patients with Mild Cognitive Impairment.Journal of medical and biological engineering · 2025Article
- Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort.GeroScience · 2024Article
- The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Review
- Alzheimer's Related Neurodegeneration Mediates Air Pollution Effects on Medial Temporal Lobe Atrophy.medRxiv : the preprint server for health sciences · 2023Article
- Is an MRI-derived anatomical measure of dementia risk also a measure of brain aging?GeroScience · 2023Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- AD-CovNet: An exploratory analysis using a hybrid deep learning model to handle data imbalance, predict fatality, and risk factors in Alzheimer's patients with COVID-19.Computers in biology and medicine · 2022Article
- A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation.Nature communications · 2022Article
- Comparing data-driven and hypothesis-driven MRI-based predictors of cognitive impairment in individuals from the Atherosclerosis Risk in Communities (ARIC) study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.Communications biology · 2022Article
- Article
- Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features.Computational intelligence and neuroscience · 2021Article
- Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model.CNS neuroscience & therapeutics · 2020Article
- Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2020Article
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Authors and funding
8 authors.
Funding
Abstract
The goal of this work is to introduce new metrics to assess risk of Alzheimer's disease (AD) which we call AD Pattern Similarity (AD-PS) scores. These metrics are the conditional probabilities modeled by large-scale regularized logistic regression. The AD-PS scores derived from structural MRI and cognitive test data were tested across different situations using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The scores were computed across groups of participants stratified by cognitive status, age and functional status. Cox proportional hazards regression was used to evaluate associations with the distribution of conversion times from mild cognitive impairment to AD. The performances of classifiers developed using data from different types of brain tissue were systematically characterized across cognitive status groups. We also explored the performance of anatomical and cognitive-anatomical composite scores generated by combining the outputs of classifiers developed using different types of data. In addition, we provide the AD-PS scores performance relative to other metrics used in the field including the Spatial Pattern of Abnormalities for Recognition of Early AD (SPARE-AD) index and total hippocampal volume for the variables examined.
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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.