ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2021
Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Brain structural indicators of β-amyloid neuropathology.Neurobiology of aging · 2024Article
- Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET.PloS one · 2024Article
- Brain health registry updates: An online longitudinal neuroscience platform.Alzheimer's & dementia : the journal of the Alzheimer's Association · 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
- Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.Life (Basel, Switzerland) · 2022Review
- Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
Abstract
introductionThis study investigated the extent to which subjective and objective data from an online registry can be analyzed using machine learning methodologies to predict the current brain amyloid beta (Aβ) status of registry participants.
methodsWe developed and optimized machine learning models using data from up to 664 registry participants. Models were assessed on their ability to predict Aβ positivity using the results of positron emission tomography as ground truth.
resultsStudy partner-assessed Everyday Cognition score was preferentially selected for inclusion in the models by a feature selection algorithm during optimization. DISCUSSION: Our results suggest that inclusion of study partner assessments would increase the ability of machine learning models to predict Aβ positivity.
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.