ArticleScientific reports2021
Machine learning methods to predict amyloid positivity using domain scores from cognitive tests.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Testing disease progression under the proportional reduction in decline in Alzheimer's disease studies.Journal of applied statistics · 2026Article
- AI-driven fusion of multimodal data for Alzheimer's disease biomarker assessment.Nature communications · 2025Article
- Predicting amyloid beta accumulation in cognitively unimpaired older adults: Cognitive assessments provide no additional utility beyond demographic and genetic factors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Blood biomarkers as surrogate endpoints in Alzheimer's disease research.Frontiers in aging neuroscience · 2025Article
- Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia.Alzheimer's research & therapy · 2024Article
- Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.Alzheimer's research & therapy · 2024Review
- Continuity corrected score confidence interval for the difference in proportions in paired data.Journal of applied statistics · 2024Article
- Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.Frontiers in neuroscience · 2024Article
- Predicting Conversion Time from Mild Cognitive Impairment to Dementia with Interval-Censored Models.Journal of Alzheimer's disease : JAD · 2024Article
- Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function.The journal of prevention of Alzheimer's disease · 2024Article
- Predicting Amyloid-β Positivity in Alzheimer's Disease: Comprehensive Analysis of Feature Selection and Machine Learning Models for Accurate Identification.International journal of online and biomedical engineering · 2023Article
- MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.Radiology · 2023Article
- Monte Carlo cross-validation for a study with binary outcome and limited sample size.BMC medical informatics and decision making · 2022Article
- Randomized two-stage optimal design for interval-censored data.Journal of biopharmaceutical statistics · 2022Article
- Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.Life (Basel, Switzerland) · 2022Review
- Conservative confidence intervals for the intraclass correlation coefficient for clustered binary data.Journal of applied statistics · 2022Article
- Disease progression from mild cognitive impairment to dementia for patients with Alzheimer's disease or Lewy body pathology.Journal of Alzheimer's disease reportsArticle
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Authors and funding
4 authors.
Funding
Abstract
Amyloid-[Formula: see text] (A[Formula: see text]) is the target in many clinical trials for Alzheimer's disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A[Formula: see text] status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the A[Formula: see text] status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features. Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores. Accurate ML prediction models can identify the proper population for AD clinical trials.
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