Identifying Cost-Effective Predictive Rules of Amyloid-β Level by Integrating Neuropsychological Tests and Plasma-Based Markers (original) (raw)
Practical algorithms for amyloid β probability in subjective or mild cognitive impairment
Judith Jaeger
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 2019
View PDFchevron_right
Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment
L. Ellingsen
European Journal of Nuclear Medicine and Molecular Imaging
View PDFchevron_right
Predicting amyloid status using self‐report information from an online research and recruitment registry: The Brain Health Registry
monica camacho
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 2020
View PDFchevron_right
Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials
Gustavo Jimenez-Maggiora
The Journal of Prevention of Alzheimer's Disease, 2020
View PDFchevron_right
A prediction model to calculate probability of Alzheimer’s disease using cerebrospinal fluid biomarkers
Jurgen Claassen
Alzheimer's & Dementia, 2013
View PDFchevron_right
A cross-sectional study in healthy elderly subjects aimed at development of an algorithm to increase identification of Alzheimer pathology for the purpose of clinical trial participation
Geert Groeneveld
Alzheimer's Research & Therapy
View PDFchevron_right
Predicting amyloid risk by machine learning algorithms based on the A4 screen data: Application to the Japanese Trial‐Ready Cohort study
Gustavo Jimenez-Maggiora
Alzheimer's & Dementia: Translational Research & Clinical Interventions, 2021
View PDFchevron_right
Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry
monica camacho
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 2021
View PDFchevron_right
Selection of amyloid positive pre-symptomatic subjects using automatic analysis of neuropsychological and MRI data for cost effective inclusion procedures in clinical trials
Didier Dormont
2017
View PDFchevron_right
Predicting conversion of brain β-amyloid positivity in amyloid-negative individuals
Hyemin Jang
Alzheimer's Research & Therapy
View PDFchevron_right
Challenges associated with biomarker-based classification systems for Alzheimer's disease
mony de Leon
Alzheimer's & dementia (Amsterdam, Netherlands), 2018
View PDFchevron_right
A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual
Anthony Bjourson
View PDFchevron_right
Assessment of a Plasma Amyloid Probability Score to Estimate Amyloid Positron Emission Tomography Findings Among Adults With Cognitive Impairment
Joel Braunstein
JAMA Network Open
View PDFchevron_right
Predicting Alzheimer disease from a blood-based biomarker profile: A 54-month follow-up
Ashley Bush
Neurology, 2016
View PDFchevron_right
Monte Carlo feature selection and rule-based models to predict Alzheimer’s disease in mild cognitive impairment
M. Kruczyk
Journal of Neural Transmission, 2012
View PDFchevron_right
Alzheimer’s Disease: The Relative Importance Diagnostic
Maryam Habadi
2020
View PDFchevron_right
Robust prediction of cognitive test scores in Alzheimer's patients
J. Andrian
2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2017
View PDFchevron_right
A diagnostic scale for Alzheimer’s disease based on cerebrospinal fluid biomarker profiles
Constance Delaby
Alzheimer's Research & Therapy, 2014
View PDFchevron_right
A machine learning-based holistic approach for diagnoses within the Alzheimer’s disease spectrum
Stefano Sensi
2020
View PDFchevron_right
A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers
Maria Carrillo
Neurology, 2016
View PDFchevron_right
Predicting the progression of Alzheimer's disease by plasma-based biomarkers
Orla Doyle
Alzheimer's & Dementia, 2012
View PDFchevron_right
Identification of Optimum Panel of Blood-based Biomarkers for Alzheimer’s Disease Diagnosis Using Machine Learning
chima eke
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
View PDFchevron_right
Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers
Hyemin Jang
Scientific Reports, 2020
View PDFchevron_right
Predictive models in Alzheimer's disease: an evaluation based on data mining techniques
International Journal of Electrical and Computer Engineering (IJECE)
International Journal of Electrical and Computer Engineering (IJECE), 2024
View PDFchevron_right
Comparative Analysis of Different Definitions of Amyloid-β Positivity to Detect Early Downstream Pathophysiological Alterations in Preclinical Alzheimer
Marta Milà Alomà
The Journal of Prevention of Alzheimer's Disease, 2020
View PDFchevron_right
Prediction of Longitudinal Cognitive Decline in Preclinical Alzheimer Disease Using Plasma Biomarkers
Rebecca Koscik
JAMA Neurology
View PDFchevron_right
Prediction of Conversion from Mild Cognitive Impairment to Alzheimer Disease Based on Bayesian Data Mining with Ensemble Learning
Karl Young
Rivista Di Neuroradiologia, 2012
View PDFchevron_right
Measurement of neurodegeneration using a multivariate early frame amyloid PET classifier
Randolph Andrews
Alzheimer's & Dementia: Translational Research & Clinical Interventions
View PDFchevron_right
Several direct and calculated biomarkers from the amyloid-β pool in blood are associated with an increased likelihood of suffering from mild cognitive impairment
ITZIAR SAN JOSÉ
View PDFchevron_right
A Machine Learning Model to Predict the Onset of Alzheimer Disease using Potential Cerebrospinal Fluid (CSF) Biomarkers
TABREJ KHAN
International Journal of Advanced Computer Science and Applications
View PDFchevron_right
Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer's Disease
Emmanuel Ifeachor, Javier Escudero, Stephen Pearson
IEEE Transactions on Biomedical Engineering, 2000
View PDFchevron_right