ArticleAlzheimer disease and associated disorders
Big Data Analytical Approaches to the NACC Dataset: Aiding Preclinical Trial Enrichment.
Article in Alzheimer disease and associated disorders. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- A Flexible-Horizon Clinical Decision Support Model for Dementia Early Detection and Risk Prediction in Secondary Care: A Responsible AI Approach.Journal of medical systems · 2026Article
- Deep survival modeling to predict future cognitive impairment in unimpaired adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Mild cognitive impairment-to-Alzheimer's dementia progression risk: the contribution of the Interceptor project.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Stratifying dementia risk factors: A prediction model and hypothesis-driven analysis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Development and Preliminary Validation of Standardized Regression-Based Change Scores as Measures of Transitional Cognitive Decline.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025Article
- Racial and ethnic differences in white matter hypointensities: The role of vascular risk factors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Machine learning models identify predictive features of patient mortality across dementia types.Communications medicine · 2024Article
- An explainable machine learning approach for Alzheimer's disease classification.Scientific reports · 2024Article
- Article
- Predictive Models and Features of Patient Mortality across Dementia Types.Research square · 2023Article
- Predicting Progression to Clinical Alzheimer's Disease Dementia Using the Random Survival Forest.Journal of Alzheimer's disease : JAD · 2023Article
- Predicting Progression from Normal to MCI and from MCI to AD Using Clinical Variables in the National Alzheimer's Coordinating Center Uniform Data Set Version 3: Application of Machine Learning Models and a Probability Calculator.The journal of prevention of Alzheimer's disease · 2023Article
- Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening.Brain sciences · 2022Article
- Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients.JAMA network open · 2021Article
- Federated Adversarial Debiasing for Fair and Transferable Representations.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2021Article
- Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review.JAMIA open · 2021Review
- Empirically defining the preclinical stages of the Alzheimer's continuum in the Alzheimer's Disease Neuroimaging Initiative.Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society · 2021Article
- The Internet-Based Conversational Engagement Clinical Trial (I-CONECT) in Socially Isolated Adults 75+ Years Old: Randomized Controlled Trial Protocol and COVID-19 Related Study Modifications.Frontiers in digital health · 2021Article
- Using multivariate base rates of low scores to understand early cognitive declines on the uniform data set 3.0 Neuropsychological Battery.Neuropsychology · 2020Article
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6 authors.
Funding
Abstract
backgroundClinical trials increasingly aim to retard disease progression during presymptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive.
methodsWe used only noninvasive clinical variables collected in the National Alzheimer's Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 years.
resultsA total of 31,872 unique subjects, 748 clinical variables, and additional 128 derived variables in NACC data sets were used. About 15 noninvasive clinical variables are identified for predicting MCI/aMCI/naMCI converters, respectively. Over 75% Receiver Operating Characteristic Area Under the Curves (ROC AUC) was achieved. By bootstrap we created a simple spreadsheet calculator which estimates the probability of developing MCI within 4 years with a 95% confidence interval.
conclusionsWe achieved reasonably high prediction accuracy using only clinical variables. The approach used here could be useful for study enrichment in preclinical trials where enrolling participants at risk of cognitive decline is critical for proving study efficacy, and also for developing a shorter assessment battery.
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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.